From f67d43a43f6c3e58922e3c2f457dd9caafebfc67 Mon Sep 17 00:00:00 2001
From: shuishen <1109946754@qq.com>
Date: Fri, 14 Aug 2026 14:20:22 +0800
Subject: [PATCH] feat:具体能力实现处理
---
apps/workbench-console/README.md | 48
.gitignore | 3
apps/workbench-console/src/data/capabilities.ts | 34
scripts/check-environment.ps1 | 11
apps/workbench-console/tsconfig.app.json | 21
baseData/演示.xlsx | 0
apps/workbench-console/src/components/ObjectDetectionPanel.vue | 37
baseData/uom-fly-zone.bin-B8O-S8Cs.gzip | 0
capabilities/01-object-detection/README.md | 103
apps/workbench-console/src/api/artifacts.ts | 101
apps/workbench-console/src/views/OverviewView.vue | 26
capabilities/15-trajectory-analysis/requirements.txt | 3
apps/workbench-console/src/components/PageHeader.vue | 10
tests/test_serve_workbench_console.py | 46
apps/workbench-console/src/styles.css | 17
apps/workbench-console/tsconfig.json | 7
apps/workbench-console/index.html | 13
capabilities/15-trajectory-analysis/run_trajectory_analysis.py | 636
apps/workbench-console/src/views/CapabilityView.vue | 22
capabilities/15-trajectory-analysis/generate_demo_inputs.py | 197
capabilities/01-object-detection/requirements.txt | 6
apps/workbench-console/src/stores/index.ts | 3
apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue | 28
apps/workbench-console/tsconfig.node.json | 12
capabilities/README.md | 20
capabilities/01-object-detection/run_detection.py | 256
apps/workbench-console/src/main.ts | 11
apps/workbench-console/package-lock.json | 2499
apps/workbench-console/src/components/TrajectoryMap.vue | 88
apps/workbench-console/package.json | 26
scripts/serve_workbench_console.py | 91
README.md | 44
baseData/no-fly-zone-Chn_xS2X.geojson | 203348 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
baseData/田墩-疫木识别（勿删）.kmz | 0
apps/workbench-console/src/components/ArtifactState.vue | 10
apps/workbench-console/src/App.vue | 60
capabilities/01-object-detection/run_geoai_vehicle_detection.py | 220
apps/workbench-console/vite.config.ts | 39
能力边界与来源说明.md | 29
AGENTS.md | 45
apps/workbench-console/src/stores/artifacts.ts | 61
capabilities/15-trajectory-analysis/README.md | 122
capabilities/15-trajectory-analysis/tests/test_demo.py | 73
PROJECT_CONTEXT.md | 145
apps/workbench-console/src/router/index.ts | 13
新对话启动说明.txt | 24
46 files changed, 208,590 insertions(+), 18 deletions(-)
diff --git a/.gitignore b/.gitignore
index de6aba5..057404d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,8 @@
dist/
build/
*.egg-info/
+*.pt
+Ultralytics/
shared/data/raw/*
shared/data/interim/*
@@ -22,4 +24,3 @@
shared/outputs/*
!**/.gitkeep
!shared/**/README.md
-
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..11b613b
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,45 @@
+# GeoAI Workbench Instructions
+
+## Scope
+
+These instructions apply to the entire repository at `E:\AllWorkProject\geoai-workbench`.
+Read `PROJECT_CONTEXT.md` before planning or changing a capability.
+
+## Project Definition
+
+- In this repository, GeoAI means workflows based on `opengeos/geoai` (`geoai-py`), not every spatial-intelligence feature.
+- Keep the capability boundary explicit:
+ - A: directly provided or documented by `geoai-py`.
+ - B: built with `geoai-py` plus geospatial or AI ecosystem libraries.
+ - C: a product/service capability that consumes GeoAI outputs.
+- Verify current APIs against the installed package, source, or official documentation. Do not label B or C work as a built-in GeoAI function.
+- Treat the user as a beginner who supplies goals, sample data, and visual feedback. Explain decisions in clear Chinese and handle implementation, environment work, debugging, and verification end to end.
+
+## Repository Conventions
+
+- Keep one capability in `capabilities/<number>-<name>/`.
+- Every capability must have a `README.md` and `requirements.txt`; add code, tests, configs, and examples only when needed.
+- Share compatible dependencies through `requirements/base.txt`. Use `.venvs/<capability>` for isolated environments when needed.
+- Use Python 3.12 for GeoAI and deep-learning capabilities. Do not install project dependencies into system Python.
+- Store reusable code in `src/geoai_common/` only after it is genuinely shared.
+- Keep raw data, processed data, weights, caches, and generated outputs out of Git and under the existing `shared/` layout.
+
+## Capability Workflow
+
+1. Read the root documentation, `PROJECT_CONTEXT.md`, and the target capability README.
+2. Classify the capability as A, B, or C and identify the exact role of `geoai-py`.
+3. Define a runnable Demo with real input, inspectable output, structured metadata, and measurable acceptance criteria.
+4. Inspect hardware and existing environments before installing dependencies. Default to CPU because no NVIDIA CUDA is available.
+5. Validate one normal and one difficult representative sample before running a full directory.
+6. Visually inspect generated images, maps, documents, or spreadsheets.
+7. Record model, package, dataset, and weight licenses independently before suggesting commercial use.
+8. Update the capability README and `PROJECT_CONTEXT.md` after verified changes.
+
+## Engineering Rules
+
+- Prefer existing project patterns and `geoai-py` APIs over new abstractions.
+- Preserve geospatial coordinates only when the input has a valid CRS and transform. Ordinary JPEG results remain pixel coordinates.
+- Report false positives, misses, runtime, and unsupported classes honestly. Do not hide limitations by lowering thresholds.
+- Do not batch-run large datasets until a representative validation indicates that the approach is suitable.
+- Do not delete or overwrite source imagery, model weights, results, or unrelated user changes.
+- Do not commit unless the user explicitly asks.
diff --git a/PROJECT_CONTEXT.md b/PROJECT_CONTEXT.md
new file mode 100644
index 0000000..2d3b4c9
--- /dev/null
+++ b/PROJECT_CONTEXT.md
@@ -0,0 +1,145 @@
+# GeoAI Workbench Current Context
+
+Last updated: 2026-08-14
+
+This file is the current project snapshot for new Codex tasks. Keep it concise and replace stale facts instead of appending a conversation diary.
+
+## Project Identity
+
+- Repository: `E:\AllWorkProject\geoai-workbench`
+- Technical foundation: [opengeos/geoai](https://github.com/opengeos/geoai), PyPI package `geoai-py`, import name `geoai`.
+- Purpose: build independently runnable GeoAI capability Demos for drone and remote-sensing workflows, then evaluate integration with existing products.
+- Product repositories in scope for later integration:
+ - `E:\AllWorkProject\drone_project\ztzf-drone-web\applications\command-center-dashboard`
+ - `E:\AllWorkProject\drone_project\ztzf-drone-web\applications\drone-web-manage`
+- Capability classification: A = direct `geoai-py`; B = GeoAI plus ecosystem libraries; C = product/service capability consuming GeoAI results.
+
+## Collaboration and Constraints
+
+- The user is a beginner and can invest about 7-8 hours per day in running Demos and giving visual feedback.
+- Codex is expected to handle environment setup, code, model selection, debugging, verification, and iteration.
+- The machine runs Windows and PowerShell with 64 GB RAM and an AMD RX 590 GME 8 GB GPU. There is no NVIDIA CUDA, so current Demos use CPU inference.
+- Use Python 3.12 for capability environments. The system also has Python 3.13, which must not replace or contaminate project environments.
+- `geoai-py` is MIT-licensed, but every dependency, dataset, and model weight needs a separate commercial-license check.
+- Current JPEG samples have no usable georeferencing. Their detections use pixel coordinates; accurate GeoJSON requires a georeferenced GeoTIFF.
+
+## Environment Snapshot
+
+Dedicated object-detection environment:
+
+`E:\AllWorkProject\geoai-workbench\.venvs\01-object-detection`
+
+| Component | Version |
+| --- | --- |
+| Python | 3.12.10 |
+| geoai-py | 0.42.0 |
+| torch | 2.13.0+cpu |
+| torchvision | 0.28.0+cpu |
+| rasterio | 1.5.1 |
+| geopandas | 1.1.4 |
+| ultralytics | 8.4.118 |
+
+`pip check` passes in this environment.
+
+Dedicated trajectory-analysis environment:
+
+`E:\AllWorkProject\geoai-workbench\.venvs\15-trajectory-analysis`
+
+| Component | Version |
+| --- | --- |
+| Python | 3.12.10 |
+| pandas | 2.3.3 |
+| geopandas | 1.1.4 |
+| shapely | 2.1.2 |
+| pyproj | 3.7.2 |
+| scikit-learn | 1.9.0 |
+| matplotlib | 3.11.1 |
+
+`geoai-py` is intentionally not installed in this environment because version 0.42.0 has no trajectory, tracking, or behavior-recognition API. The capability consumes timestamped track results that may originate from an upstream GeoAI detection/export workflow. `pip check` passes.
+
+## Local Experiment Console
+
+- Location: `apps/workbench-console/`
+- Frontend: Vue 3 + Vite + Ant Design Vue + Pinia. Trajectory maps use Cesium `1.126.0` with a local grid base layer.
+- Build: `Set-Location .\apps\workbench-console; npm install; npm run build`
+- Start command: `py -3.12 .\scripts\serve_workbench_console.py`
+- URL: `http://127.0.0.1:6173` (built console); Vite development is `http://127.0.0.1:6174/apps/workbench-console/`. Only `6xxx` ports are accepted.
+- Scope: independent, read-only local UI for this workbench. It has no code, API, account, data-upload, or task-execution link to the two drone-product repositories.
+- First release: overview plus live result pages for `01-object-detection` and `15-trajectory-analysis`; other capability pages show their registered status only.
+- File exposure: the local server permits only console assets, `shared/outputs`, and source images required for the object-detection comparison. It does not expose the rest of the repository.
+
+## Capability Status
+
+| Capability | Status | Current conclusion |
+| --- | --- | --- |
+| `00-change-detection` | Existing product capability confirmed; local Demo not implemented | The current system's orthophoto change detection already uses GeoAI. |
+| `01-object-detection` | In progress; runnable people and vehicle experiments | Tiled YOLO helps people; GeoAI NWPU substantially improves top-down vehicles; tree detection is not implemented. |
+| `15-trajectory-analysis` | Runnable CPU Demo verified | C capability consuming timestamped tracks: spatial metrics, DBSCAN and explicit behavior rules; not a built-in `geoai-py` function. |
+| `02` through `14`, `16` through `18` | Directory and initial README only | No verified local Demo yet. Start each one through `$geoai-capability-builder`. |
+
+## Object Detection Snapshot
+
+Target classes: people, vehicles, and trees.
+
+Inputs:
+
+- Directory: `shared/data/raw/01-object-detection`
+- 20 files total: 19 ordinary JPEG images and one internal DJI MPO with a `.jpeg` extension.
+- Most images are 3840 x 2160. The MPO is skipped safely.
+
+Environment and scripts:
+
+- General tiled detector: `capabilities/01-object-detection/run_detection.py`
+- GeoAI aerial vehicle detector: `capabilities/01-object-detection/run_geoai_vehicle_detection.py`
+- General model: Ultralytics `yolo11n.pt`, CPU, 1024-pixel tiles, 20% overlap, confidence 0.20.
+- Aerial vehicle model: `giswqs/nwpu-vhr10-maskrcnn:best_model.pth`, called through `geoai-py`, 512-pixel windows, 128-pixel overlap, confidence 0.30.
+
+Measured results:
+
+- Whole-image YOLO baseline across 19 JPEGs: 13 detections, including 3 people and 10 cars; this misses many small targets.
+- `DJI_20260713102047_0001_V_19.jpeg`: tiled YOLO found five person candidates, including duplicates and a false positive; the visible red vehicle was still missed by both YOLO and NWPU.
+- `DJI_20260810092727_0001_V_10.jpeg`: general YOLO found only 2-3 cars. GeoAI NWPU produced 33 raw vehicle detections and 32 after containment deduplication, with about 90 seconds CPU inference.
+- NWPU is much better for top-down aerial vehicles but still misses vehicles and can produce partial-box duplicates or other aerial-class false positives.
+
+Current technical decisions:
+
+- Do not batch-run the final pipeline across all images yet.
+- Use separate model branches: tiled YOLO for people, aerial-specific detection for vehicles, and a future tree-crown detection or segmentation model for trees.
+- Do not claim that the current COCO or NWPU model detects trees.
+- Establish a fixed manually annotated validation set before tuning or training.
+- Product use of Ultralytics and NWPU weights remains blocked on license review and accuracy evaluation.
+
+## Trajectory Analysis Snapshot
+
+Inputs and artifacts:
+
+- Generator: `capabilities/15-trajectory-analysis/generate_demo_inputs.py`
+- Analyzer: `capabilities/15-trajectory-analysis/run_trajectory_analysis.py`
+- Input contract: WGS84 timestamped CSV observations plus GeoJSON reference routes and zones, connected by a `*.case.json` manifest.
+- Generated representative inputs: `shared/data/raw/15-trajectory-analysis`
+- Inspectable outputs: `shared/outputs/15-trajectory-analysis`
+
+Measured validation:
+
+- The normal synthetic case has 2 tracks and 0 events.
+- The difficult synthetic case has 3 tracks, drops 1 duplicate timestamp, and finds exactly one stop, one route deviation, one restricted-zone event, and one gathering event.
+- Directory processing handled 5 tracks and 78 cleaned observations in about 2.8 seconds on CPU.
+- Missing required CSV columns fail cleanly with exit code 2. Both 1400 x 980 PNG results were visually inspected.
+
+Current decisions and limitations:
+
+- Keep the boundary explicit: the current Demo is C because it does not call `geoai-py`; using geospatial ecosystem libraries alone does not make it B. `geoai-py` is only a possible upstream source.
+- This is rule-based behavior detection, not learned video action recognition, and it assumes track IDs already exist.
+- Synthetic data verifies logic only. Do not claim real-world accuracy or batch-run operational data yet.
+- Next decision: obtain one normal and one difficult anonymized real track set, manually label events, then measure false positives, misses, GPS sensitivity, and per-entity thresholds.
+
+## Starting a New Capability
+
+Open a new Codex task with the repository as the working directory and use:
+
+```text
+使用 $geoai-capability-builder，创建下一个能力：<能力名称>。
+先读取 AGENTS.md 和 PROJECT_CONTEXT.md，再准备环境、Demo 输入输出和验收方式。
+```
+
+After completing verified work, update this snapshot and the capability README. Change `AGENTS.md` only when a long-lived rule changes.
diff --git a/README.md b/README.md
index cde9d01..f63c1dc 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,16 @@
# GeoAI Workbench
-GeoAI 多能力实验与产品验证工作区。每个能力都有独立目录、依赖声明和
-输入/输出约定；通用依赖、共享数据、模型权重和运行结果由根目录统一管理。
+本工作区以 [opengeos/geoai](https://github.com/opengeos/geoai) 的 `geoai-py`
+为 GeoAI 工作流基础，围绕无人机和遥感数据做实验与产品验证。这里的“GeoAI
+能力”分为三层：`geoai-py` 直接提供的功能、基于它的底层地理/AI生态组合出的
+能力、以及需要由现有产品后端和业务规则实现的产品能力。后两类不能表述为
+`geoai-py` 内置功能。
## 目录
```text
geoai-workbench/
+|-- apps/ # 本地实验控制台等独立界面
|-- capabilities/ # 一个能力一个目录
|-- requirements/ # 跨能力共享的 Python 依赖
|-- scripts/ # 环境初始化和检查脚本
@@ -20,6 +24,9 @@
## 环境策略
+- `geoai-py` 的包名是 `geoai-py`，导入名通常是 `geoai`，不是一个叫 `geoai`
+ 的操作系统或独立运行时。它要求 Python 3.12 或更高版本；本工作区固定使用
+ Python 3.12 以兼容 PyTorch 和地理库。
- 推荐 Python 3.11 或 3.12。当前电脑的 Python 3.13 可运行部分能力，但地理和
深度学习包的兼容性可能不完整。
- 根目录 `.venv` 用于轻量能力和公共开发工具。
@@ -41,5 +48,36 @@
powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\check-environment.ps1
```
-优先从 `06-spatial-reasoning`、`01-object-detection`、
+## 本地实验控制台
+
+控制台是本工作区内部的只读展示界面，独立于现有无人机产品。它读取
+`shared/outputs/` 中已有的实验结果，方便直接查看标注影像、轨迹图、事件和运行
+元数据；不会上传数据、调用外部接口或启动能力脚本。
+
+```powershell
+py -3.12 .\scripts\serve_workbench_console.py
+```
+
+浏览器访问 <http://127.0.0.1:6173>。默认端口为 `6173`，仅监听本机；其他可用的
+`6xxx` 端口可以通过 `--port` 指定。界面说明见
+`apps/workbench-console/README.md`。
+
+优先从 `01-object-detection`、`02-semantic-mapping`、
`07-risk-rule-engine` 和 `12-quality-control-refly` 开始。
+
+## 能力边界
+
+- 直接能力：目标检测、语义/实例分割、影像分类、变化检测、遥感数据下载与
+ 预处理、地理结果导出与可视化等，具体以 `geoai-py` 当前版本文档和源码为准。
+- 组合能力：空间测量、质量检测、灾害统计等，需要 `geoai-py` 加 Rasterio、
+ GeoPandas、Shapely、PyTorch 或其他专用库。
+- 产品能力：空间规则、风险评分、航线规划、集群调度、工单闭环、GeoLLM、
+ 知识图谱和模型治理，不是 `geoai-py` 单独提供的功能，需要产品服务实现。
+
+完整映射见 `capabilities/README.md` 和规划工作簿中的“能力映射”工作表。
+
+## 新对话继续工作
+
+新建 Codex 对话时，把工作目录选择为本项目根目录，然后使用个人 Skill
+`$geoai-capability-builder`。它会先读取 `AGENTS.md` 和 `PROJECT_CONTEXT.md`，
+无需复制此前的长对话。可直接参考根目录的 `新对话启动说明.txt`。
diff --git a/apps/workbench-console/README.md b/apps/workbench-console/README.md
new file mode 100644
index 0000000..dd0d4ad
--- /dev/null
+++ b/apps/workbench-console/README.md
@@ -0,0 +1,48 @@
+# GeoAI Workbench 本地实验控制台
+
+这是一个完全独立于无人机产品的本地只读控制台。它只展示本工作区已有能力的
+输入说明、运行元数据和结果工件，不上传数据、不调用外部 API，也不提供删除、
+覆盖或启动算法的操作。
+
+## 启动
+
+首次使用先安装和构建前端：
+
+```powershell
+Set-Location .\apps\workbench-console
+npm install
+npm run build
+Set-Location ..\..
+```
+
+再从仓库根目录运行：
+
+```powershell
+py -3.12 .\scripts\serve_workbench_console.py
+```
+
+然后在浏览器打开 <http://127.0.0.1:6173>。服务默认只监听本机回环地址；若端口
+已占用，可以显式传入另一个 `6xxx` 端口：
+
+```powershell
+py -3.12 .\scripts\serve_workbench_console.py --port 6174
+```
+
+开发 Vue 页面时，保持结果服务在 `6173`，并在另一个 PowerShell 中运行：
+
+```powershell
+Set-Location .\apps\workbench-console
+npm run dev
+```
+
+Vite 开发服务固定使用 `http://127.0.0.1:6174/apps/workbench-console/`，会通过本机代理读取 `6173` 的结果工件。
+
+## 目录职责
+
+- `apps/workbench-console/src/`：Vue 3 组件、路由、Pinia 状态与能力适配层。
+- `apps/workbench-console/dist/`：Vite 构建产物（不提交 Git）。
+- `scripts/serve_workbench_console.py`：只读本地文件服务；仅暴露构建后的控制台、已有输出和目标检测原图，不暴露仓库其余文件。
+- `shared/outputs/`：能力原始输出；控制台不复制、不修改这些文件。
+
+首版已接入 `01-object-detection` 和 `15-trajectory-analysis`。其他能力保留目录和
+边界状态，待其首个 Demo 产出可检查工件后再接入。
diff --git a/apps/workbench-console/index.html b/apps/workbench-console/index.html
new file mode 100644
index 0000000..b1d255f
--- /dev/null
+++ b/apps/workbench-console/index.html
@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="zh-CN">
+ <head>
+ <meta charset="UTF-8" />
+ <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+ <meta name="color-scheme" content="light" />
+ <title>GeoAI Workbench | 本地实验控制台</title>
+ </head>
+ <body>
+ <div id="app"></div>
+ <script type="module" src="/src/main.ts"></script>
+ </body>
+</html>
diff --git a/apps/workbench-console/package-lock.json b/apps/workbench-console/package-lock.json
new file mode 100644
index 0000000..1f4a2b3
--- /dev/null
+++ b/apps/workbench-console/package-lock.json
@@ -0,0 +1,2499 @@
+{
+ "name": "geoai-workbench-console",
+ "version": "0.1.0",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "name": "geoai-workbench-console",
+ "version": "0.1.0",
+ "dependencies": {
+ "@ant-design/icons-vue": "^7.0.1",
+ "ant-design-vue": "^4.2.6",
+ "cesium": "1.126.0",
+ "pinia": "^3.0.1",
+ "vue": "^3.5.13",
+ "vue-router": "^4.5.0"
+ },
+ "devDependencies": {
+ "@vitejs/plugin-vue": "^5.2.1",
+ "typescript": "^5.7.2",
+ "vite": "^6.0.5",
+ "vite-plugin-static-copy": "^2.3.0",
+ "vue-tsc": "^2.2.0"
+ }
+ },
+ "node_modules/@ant-design/colors": {
+ "version": "6.0.0",
+ "resolved": "https://registry.npmmirror.com/@ant-design/colors/-/colors-6.0.0.tgz",
+ "integrity": "sha512-qAZRvPzfdWHtfameEGP2Qvuf838NhergR35o+EuVyB5XvSA98xod5r4utvi4TJ3ywmevm290g9nsCG5MryrdWQ==",
+ "dependencies": {
+ "@ctrl/tinycolor": "^3.4.0"
+ }
+ },
+ "node_modules/@ant-design/icons-svg": {
+ "version": "4.5.0",
+ "resolved": "https://registry.npmmirror.com/@ant-design/icons-svg/-/icons-svg-4.5.0.tgz",
+ "integrity": "sha512-1BTUFyKPTBZ53MuTP8s0k5SFEXL7o3VHEOwLgzaoWKwnBeqIcqUtVshc4SKzhI6uACfqhJqBwBUE9FsWR3uULA=="
+ },
+ "node_modules/@ant-design/icons-vue": {
+ "version": "7.0.1",
+ "resolved": "https://registry.npmmirror.com/@ant-design/icons-vue/-/icons-vue-7.0.1.tgz",
+ "integrity": "sha512-eCqY2unfZK6Fe02AwFlDHLfoyEFreP6rBwAZMIJ1LugmfMiVgwWDYlp1YsRugaPtICYOabV1iWxXdP12u9U43Q==",
+ "dependencies": {
+ "@ant-design/colors": "^6.0.0",
+ "@ant-design/icons-svg": "^4.2.1"
+ },
+ "peerDependencies": {
+ "vue": ">=3.0.3"
+ }
+ },
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+ "bin": {
+ "tsc": "bin/tsc",
+ "tsserver": "bin/tsserver"
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+ "engines": {
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+ },
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+ "resolved": "https://registry.npmmirror.com/vite/-/vite-6.4.3.tgz",
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+ "dependencies": {
+ "esbuild": "^0.25.0",
+ "fdir": "^6.4.4",
+ "picomatch": "^4.0.2",
+ "postcss": "^8.5.3",
+ "rollup": "^4.34.9",
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+ "sass": "*",
+ "sass-embedded": "*",
+ "stylus": "*",
+ "sugarss": "*",
+ "terser": "^5.16.0",
+ "tsx": "^4.8.1",
+ "yaml": "^2.4.2"
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+ "@types/node": {
+ "optional": true
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+ "jiti": {
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+ }
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+ "node_modules/vite-plugin-static-copy": {
+ "version": "2.3.2",
+ "resolved": "https://registry.npmmirror.com/vite-plugin-static-copy/-/vite-plugin-static-copy-2.3.2.tgz",
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+ "dev": true,
+ "dependencies": {
+ "chokidar": "^3.5.3",
+ "fast-glob": "^3.2.11",
+ "fs-extra": "^11.1.0",
+ "p-map": "^7.0.3",
+ "picocolors": "^1.0.0"
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+ "engines": {
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+ },
+ "peerDependencies": {
+ "vite": "^5.0.0 || ^6.0.0"
+ }
+ },
+ "node_modules/vscode-uri": {
+ "version": "3.1.0",
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+ "dev": true
+ },
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+ "version": "3.5.41",
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+ "dependencies": {
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+ "@vue/compiler-sfc": "3.5.41",
+ "@vue/runtime-dom": "3.5.41",
+ "@vue/server-renderer": "3.5.41",
+ "@vue/shared": "3.5.41"
+ },
+ "peerDependencies": {
+ "typescript": "*"
+ },
+ "peerDependenciesMeta": {
+ "typescript": {
+ "optional": true
+ }
+ }
+ },
+ "node_modules/vue-router": {
+ "version": "4.6.4",
+ "resolved": "https://registry.npmmirror.com/vue-router/-/vue-router-4.6.4.tgz",
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+ "dependencies": {
+ "@vue/devtools-api": "^6.6.4"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/posva"
+ },
+ "peerDependencies": {
+ "vue": "^3.5.0"
+ }
+ },
+ "node_modules/vue-router/node_modules/@vue/devtools-api": {
+ "version": "6.6.4",
+ "resolved": "https://registry.npmmirror.com/@vue/devtools-api/-/devtools-api-6.6.4.tgz",
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+ },
+ "node_modules/vue-tsc": {
+ "version": "2.2.12",
+ "resolved": "https://registry.npmmirror.com/vue-tsc/-/vue-tsc-2.2.12.tgz",
+ "integrity": "sha512-P7OP77b2h/Pmk+lZdJ0YWs+5tJ6J2+uOQPo7tlBnY44QqQSPYvS0qVT4wqDJgwrZaLe47etJLLQRFia71GYITw==",
+ "dev": true,
+ "dependencies": {
+ "@volar/typescript": "2.4.15",
+ "@vue/language-core": "2.2.12"
+ },
+ "bin": {
+ "vue-tsc": "bin/vue-tsc.js"
+ },
+ "peerDependencies": {
+ "typescript": ">=5.0.0"
+ }
+ },
+ "node_modules/vue-types": {
+ "version": "3.0.2",
+ "resolved": "https://registry.npmmirror.com/vue-types/-/vue-types-3.0.2.tgz",
+ "integrity": "sha512-IwUC0Aq2zwaXqy74h4WCvFCUtoV0iSWr0snWnE9TnU18S66GAQyqQbRf2qfJtUuiFsBf6qp0MEwdonlwznlcrw==",
+ "dependencies": {
+ "is-plain-object": "3.0.1"
+ },
+ "engines": {
+ "node": ">=10.15.0"
+ },
+ "peerDependencies": {
+ "vue": "^3.0.0"
+ }
+ },
+ "node_modules/warning": {
+ "version": "4.0.3",
+ "resolved": "https://registry.npmmirror.com/warning/-/warning-4.0.3.tgz",
+ "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==",
+ "dependencies": {
+ "loose-envify": "^1.0.0"
+ }
+ }
+ }
+}
diff --git a/apps/workbench-console/package.json b/apps/workbench-console/package.json
new file mode 100644
index 0000000..efdebd7
--- /dev/null
+++ b/apps/workbench-console/package.json
@@ -0,0 +1,26 @@
+{
+ "name": "geoai-workbench-console",
+ "private": true,
+ "version": "0.1.0",
+ "type": "module",
+ "scripts": {
+ "dev": "vite",
+ "build": "vue-tsc -b && vite build",
+ "preview": "vite preview"
+ },
+ "dependencies": {
+ "@ant-design/icons-vue": "^7.0.1",
+ "ant-design-vue": "^4.2.6",
+ "cesium": "1.126.0",
+ "pinia": "^3.0.1",
+ "vue": "^3.5.13",
+ "vue-router": "^4.5.0"
+ },
+ "devDependencies": {
+ "@vitejs/plugin-vue": "^5.2.1",
+ "typescript": "^5.7.2",
+ "vite": "^6.0.5",
+ "vite-plugin-static-copy": "^2.3.0",
+ "vue-tsc": "^2.2.0"
+ }
+}
diff --git a/apps/workbench-console/src/App.vue b/apps/workbench-console/src/App.vue
new file mode 100644
index 0000000..8f7402b
--- /dev/null
+++ b/apps/workbench-console/src/App.vue
@@ -0,0 +1,60 @@
+<script setup lang="ts">
+import { computed } from "vue";
+import { useRoute, useRouter } from "vue-router";
+import {
+ AppstoreOutlined,
+ CheckCircleFilled,
+ CompassOutlined,
+ ExperimentOutlined
+} from "@ant-design/icons-vue";
+
+import { capabilities, verifiedCapabilities } from "@/data/capabilities";
+
+const route = useRoute();
+const router = useRouter();
+const activeKey = computed(() => route.name === "overview" ? "overview" : String(route.params.id));
+const plannedCapabilities = computed(() => capabilities.filter((item) => item.status !== "verified"));
+
+function navigate(key: string) {
+ if (key === "overview") router.push({ name: "overview" });
+ else router.push({ name: "capability", params: { id: key } });
+}
+
+function handleMenuClick(info: { key: string | number }) {
+ navigate(String(info.key));
+}
+</script>
+
+<template>
+ <a-layout class="application-frame">
+ <a-layout-sider class="application-sider" :width="252" breakpoint="lg" collapsed-width="0">
+ <div class="brand" @click="navigate('overview')">
+ <span class="brand-mark">GW</span>
+ <span><strong>GeoAI Workbench</strong><small>本地实验控制台</small></span>
+ </div>
+
+ <a-menu class="navigation-menu" theme="dark" mode="inline" :selected-keys="[activeKey]" @click="handleMenuClick">
+ <a-menu-item key="overview"><AppstoreOutlined /><span>实验总览</span></a-menu-item>
+ <a-menu-divider />
+ <a-menu-item-group key="verified" title="已验证能力">
+ <a-menu-item v-for="capability in verifiedCapabilities" :key="capability.id">
+ <ExperimentOutlined /><span>{{ capability.title }}</span>
+ </a-menu-item>
+ </a-menu-item-group>
+ <a-menu-divider />
+ <a-menu-item-group key="planned" title="能力目录">
+ <a-menu-item v-for="capability in plannedCapabilities" :key="capability.id">
+ <CompassOutlined /><span>{{ capability.title }}</span>
+ </a-menu-item>
+ </a-menu-item-group>
+ </a-menu>
+
+ <div class="sider-status">
+ <CheckCircleFilled />
+ <div><strong>仅本机读取</strong><small>无产品连接或外部服务</small></div>
+ </div>
+ </a-layout-sider>
+
+ <a-layout class="content-layout"><a-layout-content><router-view /></a-layout-content></a-layout>
+ </a-layout>
+</template>
diff --git a/apps/workbench-console/src/api/artifacts.ts b/apps/workbench-console/src/api/artifacts.ts
new file mode 100644
index 0000000..47de970
--- /dev/null
+++ b/apps/workbench-console/src/api/artifacts.ts
@@ -0,0 +1,101 @@
+export interface Detection {
+ class_id: number;
+ class_name: string;
+ confidence: number;
+ bbox_xyxy: number[];
+}
+
+export interface DetectionImage {
+ file: string;
+ annotated_file: string;
+ width: number;
+ height: number;
+ detections: Detection[];
+}
+
+export interface DetectionRun {
+ detection_count: number;
+ processed_images: number;
+ elapsed_seconds: number;
+ device: string;
+ model: string;
+ confidence: number;
+ input_dir: string;
+ notes: string[];
+}
+
+export interface TrajectoryEvent {
+ event_id: string;
+ event_type: string;
+ track_ids: string[];
+ start_time: string;
+ end_time: string;
+ duration_seconds: number;
+}
+
+export interface TrajectoryRun {
+ case_count: number;
+ track_count: number;
+ event_count: number;
+ dropped_duplicate_observations: number;
+ elapsed_seconds: number;
+ device: string;
+ input: string;
+ thresholds_by_case: Record<string, Record<string, number>>;
+}
+
+export interface TrajectorySummary {
+ track_id: string;
+ entity_type: string;
+ point_count: string;
+ distance_m: string;
+ average_speed_mps: string;
+ behavior_labels: string;
+}
+
+export const artifactUrl = (path: string) => `/${path.replace(/\\/g, "/").split("/").map(encodeURIComponent).join("/")}`;
+
+async function getJson<T>(path: string): Promise<T> {
+ const response = await fetch(artifactUrl(path), { cache: "no-store" });
+ if (!response.ok) throw new Error(`无法读取 ${path} (${response.status})`);
+ return response.json() as Promise<T>;
+}
+
+async function getText(path: string): Promise<string> {
+ const response = await fetch(artifactUrl(path), { cache: "no-store" });
+ if (!response.ok) throw new Error(`无法读取 ${path} (${response.status})`);
+ return response.text();
+}
+
+function parseCsv(text: string): TrajectorySummary[] {
+ const lines = text.trim().split(/\r?\n/);
+ const headers = lines.shift()?.replace(/^\uFEFF/, "").split(",") ?? [];
+ return lines.filter(Boolean).map((line) => {
+ const values = line.split(",");
+ return Object.fromEntries(headers.map((header, index) => [header, values[index] ?? ""])) as unknown as TrajectorySummary;
+ });
+}
+
+export async function loadDetectionArtifacts() {
+ const [run, payload] = await Promise.all([
+ getJson<DetectionRun>("shared/outputs/01-object-detection/run_metadata.json"),
+ getJson<{ images: DetectionImage[] }>("shared/outputs/01-object-detection/detections.json")
+ ]);
+ return { run, images: payload.images };
+}
+
+export async function loadTrajectoryArtifacts() {
+ const [run, difficultEvents, difficultSummary, normalSummary] = await Promise.all([
+ getJson<TrajectoryRun>("shared/outputs/15-trajectory-analysis/run_metadata.json"),
+ getJson<{ events: TrajectoryEvent[] }>("shared/outputs/15-trajectory-analysis/difficult/events.json"),
+ getText("shared/outputs/15-trajectory-analysis/difficult/trajectory_summary.csv"),
+ getText("shared/outputs/15-trajectory-analysis/normal/trajectory_summary.csv")
+ ]);
+ return {
+ run,
+ cases: {
+ difficult: { events: difficultEvents.events, summary: parseCsv(difficultSummary) },
+ normal: { events: [], summary: parseCsv(normalSummary) }
+ }
+ };
+}
diff --git a/apps/workbench-console/src/components/ArtifactState.vue b/apps/workbench-console/src/components/ArtifactState.vue
new file mode 100644
index 0000000..7b9eaa0
--- /dev/null
+++ b/apps/workbench-console/src/components/ArtifactState.vue
@@ -0,0 +1,10 @@
+<script setup lang="ts">
+import { LoadingOutlined } from "@ant-design/icons-vue";
+
+defineProps<{ loading: boolean; error: string | null }>();
+</script>
+
+<template>
+ <a-alert v-if="error" type="error" show-icon :message="error" description="请确认只读结果服务已在 127.0.0.1:6173 运行，且该能力已有输出文件。" />
+ <div v-else-if="loading" class="loading-block"><LoadingOutlined spin /><span>正在读取本地实验工件...</span></div>
+</template>
diff --git a/apps/workbench-console/src/components/ObjectDetectionPanel.vue b/apps/workbench-console/src/components/ObjectDetectionPanel.vue
new file mode 100644
index 0000000..6959579
--- /dev/null
+++ b/apps/workbench-console/src/components/ObjectDetectionPanel.vue
@@ -0,0 +1,37 @@
+<script setup lang="ts">
+import { computed, onMounted, ref } from "vue";
+import { FileImageOutlined, InfoCircleOutlined } from "@ant-design/icons-vue";
+
+import { artifactUrl } from "@/api/artifacts";
+import ArtifactState from "@/components/ArtifactState.vue";
+import { useArtifactStore } from "@/stores/artifacts";
+
+const store = useArtifactStore();
+const mode = ref<"annotated" | "original">("annotated");
+const selectedName = ref("");
+const selectedImage = computed(() => store.detectionImages.find((item) => item.file === selectedName.value) ?? store.detectionImages[0]);
+const previewSource = computed(() => {
+ if (!selectedImage.value) return "";
+ const path = mode.value === "annotated"
+ ? `shared/outputs/01-object-detection/${selectedImage.value.annotated_file}`
+ : `shared/data/raw/01-object-detection/${selectedImage.value.file}`;
+ return artifactUrl(path);
+});
+
+onMounted(async () => {
+ await store.loadDetection();
+ const mostDetections = [...store.detectionImages].sort((left, right) => right.detections.length - left.detections.length)[0];
+ selectedName.value = mostDetections?.file ?? "";
+});
+</script>
+
+<template>
+ <ArtifactState :loading="store.loading" :error="store.error" />
+ <template v-if="store.detectionRun && selectedImage">
+ <a-row :gutter="[18, 18]">
+ <a-col :xs="24" :xl="17"><section class="surface-section"><div class="section-toolbar"><a-select v-model:value="selectedName" :options="store.detectionImages.map((item) => ({ value: item.file, label: `${item.file} · ${item.detections.length} 个候选` }))" /><a-segmented v-model:value="mode" :options="[{ label: '标注结果', value: 'annotated' }, { label: '原始影像', value: 'original' }]" /></div><a-image class="detection-image" :src="previewSource" :alt="`${selectedImage.file} 预览`" /><p class="image-caption">{{ selectedImage.width }} x {{ selectedImage.height }} 像素 · {{ selectedImage.detections.length }} 个候选</p></section></a-col>
+ <a-col :xs="24" :xl="7"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="处理影像">{{ store.detectionRun.processed_images }} 张</a-descriptions-item><a-descriptions-item label="检测候选">{{ store.detectionRun.detection_count }} 个</a-descriptions-item><a-descriptions-item label="耗时">{{ store.detectionRun.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ store.detectionRun.device }}</a-descriptions-item><a-descriptions-item label="模型">{{ store.detectionRun.model }}</a-descriptions-item><a-descriptions-item label="阈值">{{ store.detectionRun.confidence }}</a-descriptions-item></a-descriptions><a-divider /><a-space direction="vertical"><a-button type="link" :href="artifactUrl('shared/outputs/01-object-detection/detections.json')" target="_blank"><FileImageOutlined />检测 JSON</a-button><a-button type="link" :href="artifactUrl('shared/outputs/01-object-detection/run_metadata.json')" target="_blank"><InfoCircleOutlined />运行元数据</a-button></a-space></section></a-col>
+ </a-row>
+ <section class="surface-section"><div class="section-heading"><div><h2>当前影像检测明细</h2><p>边界框是 JPEG 像素坐标，不含可用地理坐标。</p></div></div><a-table :data-source="selectedImage.detections" :pagination="false" row-key="bbox_xyxy" size="small" :scroll="{ x: 680 }"><a-table-column title="类别" data-index="class_name" key="class_name" /><a-table-column title="置信度" key="confidence" align="right"><template #default="{ record }">{{ (record.confidence * 100).toFixed(1) }}%</template></a-table-column><a-table-column title="像素框 x1, y1, x2, y2" key="bbox"><template #default="{ record }">{{ record.bbox_xyxy.map((value: number) => value.toFixed(1)).join(', ') }}</template></a-table-column><template #emptyText>该影像没有达到当前阈值的候选目标。</template></a-table></section>
+ </template>
+</template>
diff --git a/apps/workbench-console/src/components/PageHeader.vue b/apps/workbench-console/src/components/PageHeader.vue
new file mode 100644
index 0000000..040300c
--- /dev/null
+++ b/apps/workbench-console/src/components/PageHeader.vue
@@ -0,0 +1,10 @@
+<script setup lang="ts">
+defineProps<{ eyebrow: string; title: string; description: string }>();
+</script>
+
+<template>
+ <header class="page-header">
+ <div><p class="eyebrow">{{ eyebrow }}</p><h1>{{ title }}</h1><p class="page-description">{{ description }}</p></div>
+ <a-tag class="local-tag" color="green">本机只读</a-tag>
+ </header>
+</template>
diff --git a/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue b/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue
new file mode 100644
index 0000000..8544d8d
--- /dev/null
+++ b/apps/workbench-console/src/components/TrajectoryAnalysisPanel.vue
@@ -0,0 +1,28 @@
+<script setup lang="ts">
+import { computed, defineAsyncComponent, onMounted, ref } from "vue";
+import { DownloadOutlined } from "@ant-design/icons-vue";
+
+import { artifactUrl } from "@/api/artifacts";
+import ArtifactState from "@/components/ArtifactState.vue";
+import { useArtifactStore } from "@/stores/artifacts";
+
+const TrajectoryMap = defineAsyncComponent(() => import("@/components/TrajectoryMap.vue"));
+
+const store = useArtifactStore();
+const caseId = ref<"difficult" | "normal">("difficult");
+const currentCase = computed(() => store.trajectoryCases[caseId.value] ?? { events: [], summary: [] });
+const eventNames: Record<string, string> = { stop: "停留", route_deviation: "偏航", restricted_zone: "进入禁入区", gathering: "聚集" };
+const caseNote = computed(() => caseId.value === "difficult" ? "含停留、偏航、禁入区、聚集与重复观测。" : "连续移动且贴合参考路线，预期无事件。" );
+const difficultRules = computed(() => store.trajectoryRun?.thresholds_by_case.difficult ?? {});
+
+onMounted(() => store.loadTrajectory());
+</script>
+
+<template>
+ <ArtifactState :loading="store.loading" :error="store.error" />
+ <template v-if="store.trajectoryRun">
+ <a-row :gutter="[18, 18]"><a-col :xs="24" :xl="17"><section class="surface-section"><div class="section-toolbar"><a-segmented v-model:value="caseId" :options="[{ label: '困难样本', value: 'difficult' }, { label: '正常样本', value: 'normal' }]" /><span class="toolbar-note">{{ caseNote }}</span></div><TrajectoryMap :case-id="caseId" /></section></a-col><a-col :xs="24" :xl="7"><section class="surface-section"><h2>本次运行</h2><a-descriptions size="small" :column="1"><a-descriptions-item label="案例">{{ store.trajectoryRun.case_count }} 个</a-descriptions-item><a-descriptions-item label="轨迹">{{ store.trajectoryRun.track_count }} 条</a-descriptions-item><a-descriptions-item label="事件">{{ store.trajectoryRun.event_count }} 个</a-descriptions-item><a-descriptions-item label="重复清洗">{{ store.trajectoryRun.dropped_duplicate_observations }} 条</a-descriptions-item><a-descriptions-item label="耗时">{{ store.trajectoryRun.elapsed_seconds }} 秒</a-descriptions-item><a-descriptions-item label="设备">{{ store.trajectoryRun.device }}</a-descriptions-item></a-descriptions><a-divider /><h3>默认规则</h3><a-descriptions size="small" :column="1"><a-descriptions-item label="停留速度">{{ difficultRules.stop_speed_mps }} m/s</a-descriptions-item><a-descriptions-item label="偏航距离">{{ difficultRules.route_deviation_m }} m</a-descriptions-item><a-descriptions-item label="聚集距离">{{ difficultRules.gathering_radius_m }} m</a-descriptions-item></a-descriptions></section></a-col></a-row>
+ <a-row :gutter="[18, 18]" class="result-row"><a-col :xs="24" :xl="10"><section class="surface-section"><div class="section-heading"><div><h2>事件记录</h2><p>{{ currentCase.events.length }} 个规则事件</p></div></div><a-empty v-if="!currentCase.events.length" description="正常样本未触发规则事件" /><a-timeline v-else><a-timeline-item v-for="event in currentCase.events" :key="event.event_id" :color="event.event_type === 'gathering' ? 'green' : event.event_type === 'restricted_zone' ? 'gold' : event.event_type === 'route_deviation' ? 'purple' : 'red'"><strong>{{ eventNames[event.event_type] || event.event_type }}</strong><p>{{ event.track_ids.join('、') }}</p><small>{{ new Date(event.start_time).toLocaleString('zh-CN', { hour12: false }) }} · {{ event.duration_seconds }} 秒</small></a-timeline-item></a-timeline></section></a-col><a-col :xs="24" :xl="14"><section class="surface-section"><div class="section-heading"><div><h2>轨迹汇总</h2><p>聚类 ID 仅用于探索相似轨迹，不代表确认异常。</p></div></div><a-table :data-source="currentCase.summary" :pagination="false" row-key="track_id" size="small" :scroll="{ x: 760 }"><a-table-column title="轨迹" data-index="track_id" key="track_id" /><a-table-column title="对象" data-index="entity_type" key="entity_type" /><a-table-column title="点数" data-index="point_count" key="point_count" align="right" /><a-table-column title="距离" key="distance" align="right"><template #default="{ record }">{{ Number(record.distance_m).toFixed(1) }} m</template></a-table-column><a-table-column title="均速" key="speed" align="right"><template #default="{ record }">{{ Number(record.average_speed_mps).toFixed(2) }} m/s</template></a-table-column><a-table-column title="行为标签" data-index="behavior_labels" key="behavior_labels" /></a-table></section></a-col></a-row>
+ <section class="surface-section result-files"><a-space wrap><a-button v-for="file in ['events.json', 'events.geojson', 'trajectories.geojson', 'run_metadata.json']" :key="file" :href="artifactUrl(`shared/outputs/15-trajectory-analysis/${caseId}/${file}`)" target="_blank"><DownloadOutlined />{{ file }}</a-button></a-space></section>
+ </template>
+</template>
diff --git a/apps/workbench-console/src/components/TrajectoryMap.vue b/apps/workbench-console/src/components/TrajectoryMap.vue
new file mode 100644
index 0000000..33c8f78
--- /dev/null
+++ b/apps/workbench-console/src/components/TrajectoryMap.vue
@@ -0,0 +1,88 @@
+<script setup lang="ts">
+import { onBeforeUnmount, onMounted, ref, watch } from "vue";
+import {
+ Color,
+ ColorMaterialProperty,
+ ConstantProperty,
+ GeoJsonDataSource,
+ GridImageryProvider,
+ HeightReference,
+ PointGraphics,
+ Viewer
+} from "cesium";
+
+import { artifactUrl } from "@/api/artifacts";
+
+const props = defineProps<{ caseId: "difficult" | "normal" }>();
+const host = ref<HTMLElement>();
+const mapError = ref<string | null>(null);
+let viewer: Viewer | null = null;
+let trajectorySource: GeoJsonDataSource | null = null;
+let eventSource: GeoJsonDataSource | null = null;
+
+function eventColor(type: string) {
+ if (type === "route_deviation") return Color.fromCssColorString("#9a5da8");
+ if (type === "restricted_zone") return Color.fromCssColorString("#bc861a");
+ if (type === "gathering") return Color.fromCssColorString("#176b50");
+ return Color.fromCssColorString("#c6533f");
+}
+
+async function drawCase() {
+ if (!viewer) return;
+ try {
+ mapError.value = null;
+ if (trajectorySource) viewer.dataSources.remove(trajectorySource, true);
+ if (eventSource) viewer.dataSources.remove(eventSource, true);
+ const root = `shared/outputs/15-trajectory-analysis/${props.caseId}`;
+ trajectorySource = await GeoJsonDataSource.load(artifactUrl(`${root}/trajectories.geojson`), { clampToGround: false });
+ trajectorySource.entities.values.forEach((entity) => {
+ if (entity.polyline) {
+ entity.polyline.width = new ConstantProperty(4);
+ entity.polyline.material = new ColorMaterialProperty(Color.fromCssColorString("#176b50"));
+ }
+ });
+ eventSource = await GeoJsonDataSource.load(artifactUrl(`${root}/events.geojson`), { clampToGround: false });
+ eventSource.entities.values.forEach((entity) => {
+ const type = String(entity.properties?.event_type?.getValue() ?? "stop");
+ entity.point = new PointGraphics({
+ pixelSize: new ConstantProperty(12),
+ color: new ConstantProperty(eventColor(type)),
+ outlineColor: new ConstantProperty(Color.WHITE),
+ outlineWidth: new ConstantProperty(2),
+ heightReference: new ConstantProperty(HeightReference.NONE)
+ });
+ });
+ await viewer.dataSources.add(trajectorySource);
+ await viewer.dataSources.add(eventSource);
+ await viewer.flyTo(trajectorySource, { duration: 0 });
+ } catch (error) {
+ mapError.value = error instanceof Error ? error.message : "Cesium 地图加载失败";
+ }
+}
+
+onMounted(async () => {
+ if (!host.value) return;
+ viewer = new Viewer(host.value, {
+ animation: false,
+ baseLayerPicker: false,
+ fullscreenButton: false,
+ geocoder: false,
+ homeButton: false,
+ infoBox: false,
+ navigationHelpButton: false,
+ sceneModePicker: false,
+ selectionIndicator: false,
+ timeline: false
+ });
+ viewer.imageryLayers.removeAll();
+ viewer.imageryLayers.addImageryProvider(new GridImageryProvider({ cells: 8, glowWidth: 0 }));
+ await drawCase();
+});
+
+watch(() => props.caseId, drawCase);
+onBeforeUnmount(() => viewer?.destroy());
+</script>
+
+<template>
+ <div class="trajectory-map"><div ref="host" class="cesium-host"></div><a-alert v-if="mapError" class="map-error" type="warning" show-icon :message="mapError" /></div>
+</template>
diff --git a/apps/workbench-console/src/data/capabilities.ts b/apps/workbench-console/src/data/capabilities.ts
new file mode 100644
index 0000000..2256426
--- /dev/null
+++ b/apps/workbench-console/src/data/capabilities.ts
@@ -0,0 +1,34 @@
+export type CapabilityStatus = "verified" | "existing" | "planned";
+
+export interface CapabilityRecord {
+ id: string;
+ title: string;
+ level: "A" | "B" | "C";
+ status: CapabilityStatus;
+ note: string;
+}
+
+export const capabilities: CapabilityRecord[] = [
+ { id: "00-change-detection", title: "变化检测", level: "A", status: "existing", note: "现有产品能力已确认；本地 Demo 尚未实现。" },
+ { id: "01-object-detection", title: "地物目标检测", level: "A", status: "verified", note: "人员与车辆实验可运行，含标注影像和结构化检测结果。" },
+ { id: "02-semantic-mapping", title: "语义制图", level: "A", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "03-attribute-classification", title: "属性分类", level: "A", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "04-spatial-measurement", title: "空间测量", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "05-3d-pointcloud", title: "三维点云", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "06-spatial-reasoning", title: "空间推理", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "07-risk-rule-engine", title: "风险规则引擎", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "08-spatiotemporal-forecasting", title: "时空预测", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "09-anomaly-detection", title: "异常检测", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "10-smart-route-planning", title: "智能航线规划", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "11-fleet-scheduling", title: "集群调度", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "12-quality-control-refly", title: "质量检查与补飞", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "13-asset-health-diagnosis", title: "资产健康诊断", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "14-disaster-response", title: "灾害响应", level: "B", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "15-trajectory-analysis", title: "轨迹分析与行为识别", level: "C", status: "verified", note: "规则事件与轨迹聚类 CPU Demo 已验证。" },
+ { id: "16-geollm-assistant", title: "GeoLLM 助手", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "17-spatiotemporal-knowledge-graph", title: "时空知识图谱", level: "C", status: "planned", note: "等待首个本地 Demo。" },
+ { id: "18-reporting-model-governance", title: "报告与模型治理", level: "C", status: "planned", note: "等待首个本地 Demo。" }
+];
+
+export const verifiedCapabilities = capabilities.filter((item) => item.status === "verified");
+export const capabilityById = (id: string) => capabilities.find((item) => item.id === id);
diff --git a/apps/workbench-console/src/main.ts b/apps/workbench-console/src/main.ts
new file mode 100644
index 0000000..c055b29
--- /dev/null
+++ b/apps/workbench-console/src/main.ts
@@ -0,0 +1,11 @@
+import { createApp } from "vue";
+import Antd from "ant-design-vue";
+import "ant-design-vue/dist/reset.css";
+import "cesium/Build/Cesium/Widgets/widgets.css";
+import "./styles.css";
+
+import App from "./App.vue";
+import { router } from "./router";
+import { pinia } from "./stores";
+
+createApp(App).use(pinia).use(router).use(Antd).mount("#app");
diff --git a/apps/workbench-console/src/router/index.ts b/apps/workbench-console/src/router/index.ts
new file mode 100644
index 0000000..66634ae
--- /dev/null
+++ b/apps/workbench-console/src/router/index.ts
@@ -0,0 +1,13 @@
+import { createRouter, createWebHashHistory } from "vue-router";
+
+import CapabilityView from "@/views/CapabilityView.vue";
+import OverviewView from "@/views/OverviewView.vue";
+
+export const router = createRouter({
+ history: createWebHashHistory(),
+ routes: [
+ { path: "/", name: "overview", component: OverviewView },
+ { path: "/capability/:id", name: "capability", component: CapabilityView },
+ { path: "/:pathMatch(.*)*", redirect: "/" }
+ ]
+});
diff --git a/apps/workbench-console/src/stores/artifacts.ts b/apps/workbench-console/src/stores/artifacts.ts
new file mode 100644
index 0000000..879183a
--- /dev/null
+++ b/apps/workbench-console/src/stores/artifacts.ts
@@ -0,0 +1,61 @@
+import { defineStore } from "pinia";
+
+import {
+ loadDetectionArtifacts,
+ loadTrajectoryArtifacts,
+ type DetectionImage,
+ type DetectionRun,
+ type TrajectoryRun,
+ type TrajectorySummary,
+ type TrajectoryEvent
+} from "@/api/artifacts";
+
+interface ArtifactState {
+ detectionRun: DetectionRun | null;
+ detectionImages: DetectionImage[];
+ trajectoryRun: TrajectoryRun | null;
+ trajectoryCases: Record<string, { events: TrajectoryEvent[]; summary: TrajectorySummary[] }>;
+ loading: boolean;
+ error: string | null;
+}
+
+export const useArtifactStore = defineStore("artifacts", {
+ state: (): ArtifactState => ({
+ detectionRun: null,
+ detectionImages: [],
+ trajectoryRun: null,
+ trajectoryCases: {},
+ loading: false,
+ error: null
+ }),
+ actions: {
+ async loadDetection() {
+ if (this.detectionRun) return;
+ this.loading = true;
+ this.error = null;
+ try {
+ const result = await loadDetectionArtifacts();
+ this.detectionRun = result.run;
+ this.detectionImages = result.images;
+ } catch (error) {
+ this.error = error instanceof Error ? error.message : "目标检测结果读取失败";
+ } finally {
+ this.loading = false;
+ }
+ },
+ async loadTrajectory() {
+ if (this.trajectoryRun) return;
+ this.loading = true;
+ this.error = null;
+ try {
+ const result = await loadTrajectoryArtifacts();
+ this.trajectoryRun = result.run;
+ this.trajectoryCases = result.cases;
+ } catch (error) {
+ this.error = error instanceof Error ? error.message : "轨迹分析结果读取失败";
+ } finally {
+ this.loading = false;
+ }
+ }
+ }
+});
diff --git a/apps/workbench-console/src/stores/index.ts b/apps/workbench-console/src/stores/index.ts
new file mode 100644
index 0000000..cafea68
--- /dev/null
+++ b/apps/workbench-console/src/stores/index.ts
@@ -0,0 +1,3 @@
+import { createPinia } from "pinia";
+
+export const pinia = createPinia();
diff --git a/apps/workbench-console/src/styles.css b/apps/workbench-console/src/styles.css
new file mode 100644
index 0000000..ac01e94
--- /dev/null
+++ b/apps/workbench-console/src/styles.css
@@ -0,0 +1,17 @@
+:root { color: #1b2620; background: #f3f5f1; font-family: "Microsoft YaHei UI", "Segoe UI", Arial, sans-serif; font-synthesis: none; }
+* { box-sizing: border-box; }
+body { margin: 0; min-width: 320px; background: #f3f5f1; }
+.application-frame { min-height: 100vh; }
+.application-sider { position: sticky !important; top: 0; height: 100vh; background: #1e2923 !important; border-right: 1px solid #152019; }
+.brand { display: flex; align-items: center; gap: 10px; height: 82px; padding: 18px 20px; color: #f4f9f5; cursor: pointer; }
+.brand-mark { display: grid; width: 34px; height: 34px; place-items: center; background: #dcf0e4; color: #195d43; font-size: 12px; font-weight: 800; }
+.brand strong, .brand small { display: block; }.brand strong { font-size: 14px; letter-spacing: 0; }.brand small { color: #aac0b0; font-size: 11px; }
+.navigation-menu { height: calc(100vh - 150px); overflow-y: auto; background: #1e2923 !important; border-inline-end: 0 !important; }.navigation-menu .ant-menu-item-group-title { color: #9eb2a4 !important; font-size: 11px; font-weight: 700; letter-spacing: 0; }.navigation-menu .ant-menu-item { margin-inline: 0 !important; width: 100% !important; border-radius: 0 !important; }.navigation-menu .ant-menu-item-selected { background: #31443a !important; }.navigation-menu .ant-menu-item::after { border-inline-end-color: #79c99e !important; }
+.sider-status { position: absolute; right: 18px; bottom: 18px; left: 18px; display: flex; gap: 9px; padding-top: 14px; color: #dce8e0; border-top: 1px solid #42534a; }.sider-status .anticon { margin-top: 4px; color: #79c99e; }.sider-status strong, .sider-status small { display: block; }.sider-status strong { font-size: 12px; }.sider-status small { color: #a9bcb0; font-size: 11px; }
+.content-layout { background: #f3f5f1 !important; }.view-container { width: min(1460px, 100%); margin: 0 auto; padding: 34px 42px 58px; }.page-header { display: flex; align-items: flex-start; justify-content: space-between; gap: 24px; margin-bottom: 28px; }.eyebrow { margin: 0 0 6px; color: #176b50; font-size: 12px; font-weight: 700; letter-spacing: 0; }.page-header h1 { margin: 0 0 8px; color: #19231e; font-size: 29px; line-height: 1.2; letter-spacing: 0; }.page-description { max-width: 800px; margin: 0; color: #66716b; }.local-tag { margin: 2px 0 0 !important; border-radius: 3px !important; }
+.loading-block { display: flex; gap: 9px; align-items: center; justify-content: center; min-height: 150px; color: #66716b; }.metric-row { margin-bottom: 30px; }.metric-row .ant-statistic { min-height: 106px; padding: 17px; background: #ffffff; border: 1px solid #d9e0da; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.06); }.metric-row .ant-statistic-content { color: #1c392b; font-size: 29px; }.view-section { margin-top: 32px; }.section-heading { display: flex; justify-content: space-between; gap: 16px; margin-bottom: 14px; }.section-heading h2, .surface-section h2 { margin: 0 0 4px; color: #202b25; font-size: 18px; letter-spacing: 0; }.section-heading p, .surface-section p { margin: 0; color: #66716b; font-size: 12px; }
+.capability-card { overflow: hidden; border-radius: 4px !important; border-color: #d9e0da !important; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.07) !important; }.card-media { display: block; width: 100%; height: 260px; object-fit: cover; background: #e0e6e1; }.card-copy { padding: 18px; }.card-topline { display: flex; align-items: flex-start; justify-content: space-between; gap: 12px; }.card-topline h3 { margin: 0 0 6px; color: #202b25; font-size: 16px; letter-spacing: 0; }.card-topline p, .planned-card p { color: #66716b; }.card-copy .ant-space { display: flex; min-height: 40px; align-content: flex-start; margin-top: 14px; }.card-copy .ant-btn { margin-top: 8px; }.planned-card { height: 100%; border-radius: 4px !important; border-color: #d9e0da !important; }.planned-card p { min-height: 38px; margin: 7px 0 8px; font-size: 12px; }
+.surface-section { margin-bottom: 18px; padding: 20px; background: #ffffff; border: 1px solid #d9e0da; box-shadow: 0 9px 25px rgba(20, 43, 30, 0.06); }.section-toolbar { display: flex; align-items: center; justify-content: space-between; gap: 12px; margin-bottom: 13px; }.section-toolbar .ant-select { width: min(65%, 540px); }.detection-image { display: block; width: 100%; min-height: 390px; max-height: 660px; overflow: hidden; background: #232e28; text-align: center; }.detection-image .ant-image-img { width: 100%; max-height: 660px; object-fit: contain; }.image-caption { padding-top: 9px; }.surface-section .ant-descriptions { font-size: 12px; }.surface-section .ant-descriptions-item-label { color: #66716b; }.surface-section h3 { margin: 0 0 8px; font-size: 14px; }.result-row { margin-top: 0; }.result-files { margin-bottom: 0; }.toolbar-note { color: #66716b; font-size: 12px; text-align: right; }
+.trajectory-map { position: relative; height: 510px; overflow: hidden; background: #1b2620; }.cesium-host { width: 100%; height: 100%; }.map-error { position: absolute; right: 14px; bottom: 14px; left: 14px; }.cesium-viewer-bottom { display: none !important; }.cesium-viewer-toolbar { top: 10px; right: 10px; }.ant-empty { margin: 70px 0; }.ant-empty-description p { color: #66716b; }
+@media (max-width: 991px) { .application-sider { position: static !important; height: auto; }.navigation-menu { height: auto; max-height: 290px; }.sider-status { display: none; }.view-container { padding: 25px 22px 42px; }.detection-image { min-height: 270px; }.trajectory-map { height: 440px; } }
+@media (max-width: 640px) { .view-container { padding: 20px 14px 34px; }.page-header, .section-toolbar { align-items: stretch; flex-direction: column; }.page-header h1 { font-size: 24px; }.local-tag { align-self: flex-start; }.section-toolbar .ant-select { width: 100%; }.toolbar-note { text-align: left; }.surface-section { padding: 14px; }.trajectory-map { height: 380px; }.card-media { height: 205px; } }
diff --git a/apps/workbench-console/src/views/CapabilityView.vue b/apps/workbench-console/src/views/CapabilityView.vue
new file mode 100644
index 0000000..8cf694c
--- /dev/null
+++ b/apps/workbench-console/src/views/CapabilityView.vue
@@ -0,0 +1,22 @@
+<script setup lang="ts">
+import { computed } from "vue";
+import { useRoute } from "vue-router";
+
+import PageHeader from "@/components/PageHeader.vue";
+import ObjectDetectionPanel from "@/components/ObjectDetectionPanel.vue";
+import TrajectoryAnalysisPanel from "@/components/TrajectoryAnalysisPanel.vue";
+import { capabilityById } from "@/data/capabilities";
+
+const route = useRoute();
+const capability = computed(() => capabilityById(String(route.params.id)));
+</script>
+
+<template>
+ <div class="view-container" v-if="capability">
+ <PageHeader :eyebrow="`CAPABILITY ${capability.id} / ${capability.level}`" :title="capability.title" :description="capability.note" />
+ <ObjectDetectionPanel v-if="capability.id === '01-object-detection'" />
+ <TrajectoryAnalysisPanel v-else-if="capability.id === '15-trajectory-analysis'" />
+ <a-empty v-else description="等待第一个可验证 Demo"><template #description><h2>尚无本地结果工件</h2><p>该能力已登记在目录中，但尚未产生可视化结果、结构化输出或运行元数据。</p></template></a-empty>
+ </div>
+ <div v-else class="view-container"><a-result status="404" title="未找到能力页面" sub-title="请从左侧目录选择已登记的能力。" /></div>
+</template>
diff --git a/apps/workbench-console/src/views/OverviewView.vue b/apps/workbench-console/src/views/OverviewView.vue
new file mode 100644
index 0000000..0a2dc9f
--- /dev/null
+++ b/apps/workbench-console/src/views/OverviewView.vue
@@ -0,0 +1,26 @@
+<script setup lang="ts">
+import { computed, onMounted } from "vue";
+import { useRouter } from "vue-router";
+import { EyeOutlined, ExperimentOutlined } from "@ant-design/icons-vue";
+
+import ArtifactState from "@/components/ArtifactState.vue";
+import PageHeader from "@/components/PageHeader.vue";
+import { capabilities, verifiedCapabilities } from "@/data/capabilities";
+import { artifactUrl } from "@/api/artifacts";
+import { useArtifactStore } from "@/stores/artifacts";
+
+const store = useArtifactStore();
+const router = useRouter();
+const planned = computed(() => capabilities.filter((item) => item.status === "planned"));
+
+onMounted(async () => Promise.all([store.loadDetection(), store.loadTrajectory()]));
+function openCapability(id: string) { router.push({ name: "capability", params: { id } }); }
+</script>
+
+<template>
+ <div class="view-container"><PageHeader eyebrow="WORKBENCH / OVERVIEW" title="实验结果一眼可见" description="独立于无人机产品的本地控制台。它只读取当前工作区已有结果，不上传数据，也不会触发算法运行。" /><ArtifactState :loading="store.loading" :error="store.error" />
+ <a-row :gutter="[14, 14]" class="metric-row"><a-col :xs="12" :lg="6"><a-statistic title="能力目录" :value="capabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="已验证 Demo" :value="verifiedCapabilities.length" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="最近检测结果" :value="store.detectionRun?.detection_count ?? 0" suffix="个" /></a-col><a-col :xs="12" :lg="6"><a-statistic title="最近轨迹事件" :value="store.trajectoryRun?.event_count ?? 0" suffix="个" /></a-col></a-row>
+ <section class="view-section"><div class="section-heading"><div><h2>已验证能力</h2><p>从实验结果进入，不需要再查找输出目录。</p></div></div><a-row :gutter="[18, 18]"><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/01-object-detection/annotated/DJI_20260810092727_0001_V_10.jpeg')" alt="目标检测标注图" /><div class="card-copy"><div class="card-topline"><div><h3>地物目标检测</h3><p>原图与标注图切换、检测框明细与运行参数。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">A：直接能力</a-tag><a-tag>{{ store.detectionRun?.processed_images ?? 0 }} 张影像</a-tag><a-tag>{{ store.detectionRun?.detection_count ?? 0 }} 个候选</a-tag></a-space><a-button type="primary" @click="openCapability('01-object-detection')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col><a-col :xs="24" :xl="12"><a-card class="capability-card" :body-style="{ padding: '0' }"><img class="card-media" :src="artifactUrl('shared/outputs/15-trajectory-analysis/difficult/analysis.png')" alt="轨迹分析图" /><div class="card-copy"><div class="card-topline"><div><h3>轨迹分析与行为识别</h3><p>Cesium 轨迹地图、规则事件和轨迹汇总。</p></div><a-tag color="green">已验证</a-tag></div><a-space wrap><a-tag color="gold">C：产品能力</a-tag><a-tag>{{ store.trajectoryRun?.track_count ?? 0 }} 条轨迹</a-tag><a-tag>{{ store.trajectoryRun?.event_count ?? 0 }} 个事件</a-tag></a-space><a-button type="primary" @click="openCapability('15-trajectory-analysis')"><EyeOutlined />查看实验结果</a-button></div></a-card></a-col></a-row></section>
+ <section class="view-section"><div class="section-heading"><div><h2>待实现能力</h2><p>首个 Demo 产生可检查工件后，可以按相同方式接入控制台。</p></div></div><a-row :gutter="[14, 14]"><a-col v-for="capability in planned.slice(0, 8)" :key="capability.id" :xs="24" :md="12" :xl="6"><a-card size="small" class="planned-card"><div class="card-topline"><h3>{{ capability.title }}</h3><a-tag>{{ capability.level }}</a-tag></div><p>{{ capability.note }}</p><a-button type="link" size="small" @click="openCapability(capability.id)"><ExperimentOutlined />查看状态</a-button></a-card></a-col></a-row></section>
+ </div>
+</template>
diff --git a/apps/workbench-console/tsconfig.app.json b/apps/workbench-console/tsconfig.app.json
new file mode 100644
index 0000000..5158f28
--- /dev/null
+++ b/apps/workbench-console/tsconfig.app.json
@@ -0,0 +1,21 @@
+{
+ "include": ["src/**/*.ts", "src/**/*.tsx", "src/**/*.vue"],
+ "exclude": ["src/**/__tests__/*"],
+ "compilerOptions": {
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
+ "target": "ES2021",
+ "useDefineForClassFields": true,
+ "module": "ESNext",
+ "lib": ["ES2021", "DOM", "DOM.Iterable"],
+ "moduleResolution": "Bundler",
+ "allowImportingTsExtensions": true,
+ "verbatimModuleSyntax": true,
+ "moduleDetection": "force",
+ "noEmit": true,
+ "jsx": "preserve",
+ "strict": true,
+ "skipLibCheck": true,
+ "baseUrl": ".",
+ "paths": { "@/*": ["./src/*"] }
+ }
+}
diff --git a/apps/workbench-console/tsconfig.json b/apps/workbench-console/tsconfig.json
new file mode 100644
index 0000000..1ffef60
--- /dev/null
+++ b/apps/workbench-console/tsconfig.json
@@ -0,0 +1,7 @@
+{
+ "files": [],
+ "references": [
+ { "path": "./tsconfig.app.json" },
+ { "path": "./tsconfig.node.json" }
+ ]
+}
diff --git a/apps/workbench-console/tsconfig.node.json b/apps/workbench-console/tsconfig.node.json
new file mode 100644
index 0000000..0a56408
--- /dev/null
+++ b/apps/workbench-console/tsconfig.node.json
@@ -0,0 +1,12 @@
+{
+ "compilerOptions": {
+ "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
+ "target": "ES2022",
+ "module": "ESNext",
+ "moduleResolution": "Bundler",
+ "noEmit": true,
+ "strict": true,
+ "skipLibCheck": true
+ },
+ "include": ["vite.config.ts"]
+}
diff --git a/apps/workbench-console/vite.config.ts b/apps/workbench-console/vite.config.ts
new file mode 100644
index 0000000..c690248
--- /dev/null
+++ b/apps/workbench-console/vite.config.ts
@@ -0,0 +1,39 @@
+import { fileURLToPath, URL } from "node:url";
+
+import { defineConfig } from "vite";
+import vue from "@vitejs/plugin-vue";
+import { viteStaticCopy } from "vite-plugin-static-copy";
+
+const consoleBase = "/apps/workbench-console/";
+
+export default defineConfig({
+ base: consoleBase,
+ define: {
+ CESIUM_BASE_URL: JSON.stringify(`${consoleBase}cesium`)
+ },
+ plugins: [
+ vue(),
+ viteStaticCopy({
+ targets: [
+ { src: "node_modules/cesium/Build/Cesium/Workers", dest: "cesium" },
+ { src: "node_modules/cesium/Build/Cesium/ThirdParty", dest: "cesium" },
+ { src: "node_modules/cesium/Build/Cesium/Assets", dest: "cesium" },
+ { src: "node_modules/cesium/Build/Cesium/Widgets", dest: "cesium" }
+ ]
+ })
+ ],
+ resolve: {
+ alias: {
+ "@": fileURLToPath(new URL("./src", import.meta.url)),
+ "@zip.js/zip.js/lib/zip-no-worker.js": fileURLToPath(new URL("./node_modules/@zip.js/zip.js/index.js", import.meta.url))
+ }
+ },
+ server: {
+ host: "127.0.0.1",
+ port: 6174,
+ strictPort: true,
+ proxy: {
+ "/shared": "http://127.0.0.1:6173"
+ }
+ }
+});
diff --git a/baseData/no-fly-zone-Chn_xS2X.geojson b/baseData/no-fly-zone-Chn_xS2X.geojson
new file mode 100644
index 0000000..1db3278
--- /dev/null
+++ b/baseData/no-fly-zone-Chn_xS2X.geojson
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+ "crs": {
+ "type": "name",
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+ }
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diff --git a/capabilities/01-object-detection/README.md b/capabilities/01-object-detection/README.md
index 8a90073..4bb47f7 100644
--- a/capabilities/01-object-detection/README.md
+++ b/capabilities/01-object-detection/README.md
@@ -1,6 +1,101 @@
-# Object Detection
+# 地物目标检测
-- 输入：无人机照片或视频帧。
-- 输出：目标框、类别、置信度和位置。
-- 首个 Demo：10–30 张图片的预训练模型推理与 JSON 导出。
+## 目标
+先做一个可在本机 CPU 运行的预训练模型 Demo：输入无人机照片，输出带目标框、类别和置信度的标注图片，以及结构化 JSON。第二阶段再支持正射 GeoTIFF 切片，并把像素坐标转换为经纬度后输出 GeoJSON，便于接入现有 Cesium 地图和算法管理模块。
+
+本能力使用 [opengeos/geoai](https://github.com/opengeos/geoai) 发布的 `geoai-py` 作为 GeoAI 工作流层。它负责把地理影像处理、AI 推理、地理结果和可视化串起来；PyTorch、Rasterio、GeoPandas 等仍是它和本 Demo 的底层运行依赖。
+
+## 当前范围
+
+首版优先识别人员、车辆。`geoai-py` 自带的 NWPU-VHR10 预训练模型面向航空影像，并提供统一的 `vehicle` 类，但不包含人员和树木；因此当前采用“切片 YOLO 检人员 + GeoAI NWPU 检车辆”的分支方案。树木通常需要专门的树冠/单木数据集和模型微调。
+
+## 需要你准备的内容
+
+1. **目标清单**：请先确定最关心的 3–5 类目标，例如人员、车辆、挖掘机、船只、建筑物。
+2. **样例图片**：准备 10–30 张无人机 JPG/PNG，尽量包含不同高度、角度、天气和目标大小。不要使用涉密或未经授权的照片。
+3. **正射样例（第二阶段）**：准备 1–2 份 GeoTIFF，并记录坐标系、分辨率和拍摄时间。
+4. **输出接入约定**：确认结果需要保存为 JSON/GeoJSON，还是还要生成 Cesium 可加载的 GeoJSON/KML。
+5. **验收样例**：从图片中挑选 5 张作为固定验收集，不参与后续调参。
+
+## 目录约定
+
+```text
+01-object-detection/
+|-- README.md
+|-- requirements.txt
+|-- src/ # 推理和结果转换代码
+|-- scripts/ # 一键运行脚本
+|-- tests/ # 单元测试
+shared/
+|-- data/raw/01-object-detection/ # 原始图片，只存本地
+|-- data/processed/01-object-detection/ # 切片或预处理结果
+|-- models/01-object-detection/ # 模型权重，不提交 Git
+`-- outputs/01-object-detection/ # 标注图、JSON、GeoJSON
+```
+
+## 环境与硬件
+
+- 使用 Python 3.12 专用环境：`.venvs/01-object-detection`。
+- 当前电脑是 AMD RX 590 GME 8GB，不具备 NVIDIA CUDA；首版使用 CPU 推理。
+- 64GB 内存足够运行 10–30 张图片的 Demo。大尺寸正射影像需要切片，不能一次性全部载入内存。
+- 模型权重和 Python 包必须记录版本与许可证，产品使用前检查是否允许商用。
+- `geoai-py` 项目本身为 MIT 许可证；它依赖的模型权重、数据集和第三方库仍需分别核查许可证。`ultralytics` 等可选底层组件的许可证不能由 GeoAI 的 MIT 许可证自动覆盖。
+
+## 预期输入输出
+
+输入：JPG/PNG 图片，或第二阶段的 GeoTIFF。
+
+输出：
+
+- `annotated/`：画出检测框和标签的图片；
+- `detections.json`：图片名、类别、置信度、像素框坐标；
+- `detections.geojson`：正射影像场景下的地理框或中心点；
+- `run_metadata.json`：模型版本、阈值、运行时间、设备（CPU/DirectML）。
+
+## 首版验收标准
+
+- 能通过命令行处理单张图片和一个图片目录；
+- 输出图片能看到检测框、类别和置信度；
+- JSON 字段固定且可被前端读取；
+- 对固定 5 张验收图片，结果可重复生成；
+- 无 GPU 时能正常运行，并对超大图片给出清晰错误提示或自动缩放；
+- 运行日志包含模型、置信度阈值、输入数量、输出路径和耗时。
+
+## 预计开发顺序
+
+1. 创建专用环境并安装 PyTorch、torchvision、Ultralytics；
+2. 下载并登记一个允许研究/商用核查的预训练权重；
+3. 完成单图推理；
+4. 完成目录批处理、标注图和 JSON 导出；
+5. 用你的无人机图片验证并调整阈值；
+6. 增加 GeoTIFF 切片和 GeoJSON 坐标转换；
+7. 最后再做 FastAPI 接口和现有前端接入。
+
+## 当前 Demo 运行命令
+
+在项目根目录执行人员/通用车辆切片基线：
+
+```powershell
+.\.venvs\01-object-detection\Scripts\python.exe .\capabilities\01-object-detection\run_detection.py
+```
+
+默认使用 CPU、`yolo11n.pt`、1024 像素切片、20% 重叠和置信度阈值 0.20。
+结果写入 `shared/outputs/01-object-detection/`。
+
+执行 GeoAI NWPU-VHR10 航拍车辆检测：
+
+```powershell
+.\.venvs\01-object-detection\Scripts\python.exe .\capabilities\01-object-detection\run_geoai_vehicle_detection.py
+```
+
+该脚本默认使用 CPU、512 像素滑窗、128 像素重叠和置信度阈值 0.30，结果写入
+`shared/outputs/01-object-detection/geoai-vehicles/`。首次运行会自动下载约 98 MB
+的模型权重。
+
+## 当前验证结论
+
+- `DJI_20260810092727_0001_V_10.jpeg`：通用 YOLO 检出 2–3 辆车；GeoAI NWPU 检出 33 个车辆候选，CPU 推理约 90 秒，明显改善俯视小车辆漏检。
+- `DJI_20260713102047_0001_V_19.jpeg`：切片 YOLO 检出多个人员，但两种模型均未检出右上角红色汽车；该近景车顶外观仍需要更匹配的航拍数据或本项目样本微调。
+- NWPU 会产生少量其他航拍类别误检，正式 Demo 只保留 `vehicle`；脚本还会过滤被高置信度整车框大部分包含的重复局部框。
+- 树木不属于当前两个模型的有效类别，必须单独建设树冠检测/分割分支。
diff --git a/capabilities/01-object-detection/requirements.txt b/capabilities/01-object-detection/requirements.txt
index 5a1c47e..a5badde 100644
--- a/capabilities/01-object-detection/requirements.txt
+++ b/capabilities/01-object-detection/requirements.txt
@@ -1,5 +1,9 @@
-r ../../requirements/base.txt
+# GeoAI workflow layer: geospatial image preparation, detection helpers,
+# georeferenced output and visualization utilities.
+geoai-py>=0.42,<0.43
torch>=2.4,<3
torchvision>=0.19,<1
ultralytics>=8.3,<9
-
+rasterio>=1.4,<2
+geopandas>=1,<2
diff --git a/capabilities/01-object-detection/run_detection.py b/capabilities/01-object-detection/run_detection.py
new file mode 100644
index 0000000..e337b07
--- /dev/null
+++ b/capabilities/01-object-detection/run_detection.py
@@ -0,0 +1,256 @@
+"""Run the first GeoAI object-detection baseline on drone images.
+
+This baseline uses the opengeos/geoai environment plus a general-purpose
+Ultralytics model. It intentionally keeps georeferenced GeoTIFF processing for
+the next stage, because the current JPEGs do not carry usable GPS metadata.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import tempfile
+import time
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from PIL import Image
+
+
+IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png"}
+COCO_TARGET_CLASS_IDS = [0, 1, 2, 3, 5, 7]
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[2]
+ default_input = root / "shared" / "data" / "raw" / "01-object-detection"
+ default_output = root / "shared" / "outputs" / "01-object-detection"
+ parser = argparse.ArgumentParser(description="Detect objects in drone images.")
+ parser.add_argument("--input", type=Path, default=default_input)
+ parser.add_argument("--output", type=Path, default=default_output)
+ parser.add_argument("--model", default="yolo11n.pt", help="Ultralytics model name or path.")
+ parser.add_argument("--confidence", type=float, default=0.20)
+ parser.add_argument("--image-size", type=int, default=1024, help="Model input size for each tile.")
+ parser.add_argument("--tile-size", type=int, default=1024, help="Pixel size of the sliding detection window.")
+ parser.add_argument("--tile-overlap", type=float, default=0.20, help="Overlap ratio between adjacent windows.")
+ return parser.parse_args()
+
+
+def load_image_paths(input_dir: Path) -> tuple[list[Path], list[dict[str, str]]]:
+ paths: list[Path] = []
+ skipped: list[dict[str, str]] = []
+ candidates = [input_dir] if input_dir.is_file() else sorted(input_dir.iterdir())
+ for path in candidates:
+ if not path.is_file() or path.suffix.lower() not in IMAGE_SUFFIXES:
+ continue
+ try:
+ with Image.open(path) as image:
+ image.verify()
+ if image.format == "MPO":
+ skipped.append({"file": path.name, "reason": "MPO is not supported in baseline"})
+ continue
+ except Exception as exc: # pragma: no cover - depends on source files
+ skipped.append({"file": path.name, "reason": f"unreadable: {exc}"})
+ continue
+ paths.append(path)
+ return paths, skipped
+
+
+def tile_starts(length: int, tile_size: int, overlap: float) -> list[int]:
+ if not 0 <= overlap < 1:
+ raise ValueError("tile-overlap must be between 0 and 1")
+ if length <= tile_size:
+ return [0]
+ stride = max(1, int(tile_size * (1 - overlap)))
+ starts = list(range(0, length - tile_size + 1, stride))
+ last = length - tile_size
+ if starts[-1] != last:
+ starts.append(last)
+ return starts
+
+
+def intersection_over_union(box_a: list[float], box_b: list[float]) -> float:
+ left = max(box_a[0], box_b[0])
+ top = max(box_a[1], box_b[1])
+ right = min(box_a[2], box_b[2])
+ bottom = min(box_a[3], box_b[3])
+ intersection = max(0.0, right - left) * max(0.0, bottom - top)
+ area_a = max(0.0, box_a[2] - box_a[0]) * max(0.0, box_a[3] - box_a[1])
+ area_b = max(0.0, box_b[2] - box_b[0]) * max(0.0, box_b[3] - box_b[1])
+ union = area_a + area_b - intersection
+ return intersection / union if union else 0.0
+
+
+def intersection_over_smaller(box_a: list[float], box_b: list[float]) -> float:
+ left = max(box_a[0], box_b[0])
+ top = max(box_a[1], box_b[1])
+ right = min(box_a[2], box_b[2])
+ bottom = min(box_a[3], box_b[3])
+ intersection = max(0.0, right - left) * max(0.0, bottom - top)
+ area_a = max(0.0, box_a[2] - box_a[0]) * max(0.0, box_a[3] - box_a[1])
+ area_b = max(0.0, box_b[2] - box_b[0]) * max(0.0, box_b[3] - box_b[1])
+ smaller_area = min(area_a, area_b)
+ return intersection / smaller_area if smaller_area else 0.0
+
+
+def class_aware_nms(
+ detections: list[dict[str, Any]],
+ iou_threshold: float = 0.50,
+ containment_threshold: float = 0.80,
+) -> list[dict[str, Any]]:
+ kept: list[dict[str, Any]] = []
+ for candidate in sorted(detections, key=lambda item: item["confidence"], reverse=True):
+ if all(
+ candidate["class_id"] != existing["class_id"]
+ or (
+ intersection_over_union(candidate["bbox_xyxy"], existing["bbox_xyxy"]) < iou_threshold
+ and intersection_over_smaller(candidate["bbox_xyxy"], existing["bbox_xyxy"])
+ < containment_threshold
+ )
+ for existing in kept
+ ):
+ kept.append(candidate)
+ return kept
+
+
+def predict_tiled(model: Any, image_path: Path, args: argparse.Namespace, np: Any) -> tuple[list[dict[str, Any]], int, int]:
+ with Image.open(image_path) as source:
+ image = source.convert("RGB")
+ width, height = image.size
+ detections: list[dict[str, Any]] = []
+ names: dict[int, str] = {}
+ for y0 in tile_starts(height, args.tile_size, args.tile_overlap):
+ for x0 in tile_starts(width, args.tile_size, args.tile_overlap):
+ tile = image.crop((x0, y0, min(x0 + args.tile_size, width), min(y0 + args.tile_size, height)))
+ result = model.predict(
+ source=np.asarray(tile),
+ device="cpu",
+ imgsz=args.image_size,
+ conf=args.confidence,
+ classes=COCO_TARGET_CLASS_IDS,
+ max_det=100,
+ verbose=False,
+ )[0]
+ names = result.names
+ if result.boxes is None:
+ continue
+ for box in result.boxes:
+ class_id = int(box.cls.item())
+ local_box = [float(value) for value in box.xyxy[0].tolist()]
+ detections.append(
+ {
+ "class_id": class_id,
+ "class_name": names[class_id],
+ "confidence": round(float(box.conf.item()), 4),
+ "bbox_xyxy": [
+ round(local_box[0] + x0, 2),
+ round(local_box[1] + y0, 2),
+ round(local_box[2] + x0, 2),
+ round(local_box[3] + y0, 2),
+ ],
+ }
+ )
+ return class_aware_nms(detections), width, height
+
+
+def draw_detections(image_path: Path, detections: list[dict[str, Any]], output_path: Path) -> None:
+ import cv2
+ import numpy as np
+
+ canvas = cv2.cvtColor(np.asarray(Image.open(image_path).convert("RGB")), cv2.COLOR_RGB2BGR)
+ for detection in detections:
+ x1, y1, x2, y2 = [int(round(value)) for value in detection["bbox_xyxy"]]
+ label = f"{detection['class_name']} {detection['confidence']:.2f}"
+ cv2.rectangle(canvas, (x1, y1), (x2, y2), (255, 80, 0), 4)
+ cv2.putText(canvas, label, (x1, max(30, y1 - 8)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 80, 0), 2)
+ cv2.imwrite(str(output_path), canvas)
+
+
+def main() -> int:
+ args = parse_args()
+ input_dir = args.input.resolve()
+ output_dir = args.output.resolve()
+ annotated_dir = output_dir / "annotated"
+ annotated_dir.mkdir(parents=True, exist_ok=True)
+ cache_root = Path(tempfile.gettempdir()) / "geoai-object-detection"
+ os.environ.setdefault("YOLO_CONFIG_DIR", str(cache_root / "ultralytics"))
+ os.environ.setdefault("MPLCONFIGDIR", str(cache_root / "matplotlib"))
+ if not input_dir.exists():
+ print(f"Input directory does not exist: {input_dir}", file=sys.stderr)
+ return 2
+
+ image_paths, skipped = load_image_paths(input_dir)
+ if not image_paths:
+ print("No supported images found.", file=sys.stderr)
+ return 2
+
+ # Imports happen after argument validation so the CLI can explain path
+ # mistakes without requiring the heavyweight ML stack.
+ import geoai
+ import numpy as np
+ import torch
+ import ultralytics
+ from ultralytics import YOLO
+
+ started = time.perf_counter()
+ model = YOLO(args.model)
+ detections: list[dict[str, Any]] = []
+ for image_path in image_paths:
+ image_detections, width, height = predict_tiled(model, image_path, args, np)
+ annotated_path = annotated_dir / image_path.name
+ draw_detections(image_path, image_detections, annotated_path)
+ detections.append(
+ {
+ "file": image_path.name,
+ "annotated_file": str(annotated_path.relative_to(output_dir)),
+ "width": width,
+ "height": height,
+ "detections": image_detections,
+ }
+ )
+
+ elapsed = round(time.perf_counter() - started, 3)
+ (output_dir / "detections.json").write_text(
+ json.dumps({"images": detections}, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ metadata = {
+ "created_at": datetime.now(UTC).isoformat(),
+ "geoai_package": getattr(geoai, "__version__", "unknown"),
+ "ultralytics": ultralytics.__version__,
+ "torch": torch.__version__,
+ "device": "cpu",
+ "cuda_available": bool(torch.cuda.is_available()),
+ "model": args.model,
+ "confidence": args.confidence,
+ "image_size": args.image_size,
+ "tile_size": args.tile_size,
+ "tile_overlap": args.tile_overlap,
+ "merge_iou_threshold": 0.50,
+ "merge_containment_threshold": 0.80,
+ "target_class_ids": COCO_TARGET_CLASS_IDS,
+ "target_class_note": "COCO person, bicycle, car, motorcycle, bus and truck; tree is not a COCO class.",
+ "input_dir": str(input_dir),
+ "processed_images": len(image_paths),
+ "skipped_images": skipped,
+ "detection_count": sum(len(item["detections"]) for item in detections),
+ "elapsed_seconds": elapsed,
+ "notes": [
+ "This is a baseline for people and common vehicle classes.",
+ "The default COCO model does not provide a tree class.",
+ "JPEG inputs have no usable GPS metadata; GeoJSON is deferred to GeoTIFF stage.",
+ ],
+ }
+ (output_dir / "run_metadata.json").write_text(
+ json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ print(f"Processed {len(image_paths)} images; detections={metadata['detection_count']}; elapsed={elapsed}s")
+ print(f"Annotated images: {annotated_dir}")
+ print(f"JSON: {output_dir / 'detections.json'}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/capabilities/01-object-detection/run_geoai_vehicle_detection.py b/capabilities/01-object-detection/run_geoai_vehicle_detection.py
new file mode 100644
index 0000000..0ea2144
--- /dev/null
+++ b/capabilities/01-object-detection/run_geoai_vehicle_detection.py
@@ -0,0 +1,220 @@
+"""Detect aerial vehicles with the pretrained model bundled by geoai-py."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import tempfile
+import time
+from collections import Counter
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from PIL import Image
+
+
+IMAGE_SUFFIXES = {".jpg", ".jpeg", ".png", ".tif", ".tiff"}
+VEHICLE_CLASS_ID = 10
+CONTAINMENT_THRESHOLD = 0.80
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[2]
+ parser = argparse.ArgumentParser(description="Detect vehicles in aerial images with geoai-py.")
+ parser.add_argument(
+ "--input",
+ type=Path,
+ default=root / "shared" / "data" / "raw" / "01-object-detection",
+ )
+ parser.add_argument(
+ "--output",
+ type=Path,
+ default=root / "shared" / "outputs" / "01-object-detection" / "geoai-vehicles",
+ )
+ parser.add_argument("--confidence", type=float, default=0.30)
+ parser.add_argument("--window-size", type=int, default=512)
+ parser.add_argument("--overlap", type=int, default=128)
+ parser.add_argument("--nms-threshold", type=float, default=0.30)
+ parser.add_argument("--batch-size", type=int, default=4)
+ return parser.parse_args()
+
+
+def load_image_paths(input_path: Path) -> tuple[list[Path], list[dict[str, str]]]:
+ candidates = [input_path] if input_path.is_file() else sorted(input_path.iterdir())
+ paths: list[Path] = []
+ skipped: list[dict[str, str]] = []
+ for path in candidates:
+ if not path.is_file() or path.suffix.lower() not in IMAGE_SUFFIXES:
+ continue
+ try:
+ with Image.open(path) as image:
+ image.verify()
+ if image.format == "MPO":
+ skipped.append({"file": path.name, "reason": "MPO is not supported"})
+ continue
+ except Exception as exc:
+ skipped.append({"file": path.name, "reason": f"unreadable: {exc}"})
+ continue
+ paths.append(path)
+ return paths, skipped
+
+
+def intersection_over_smaller(box_a: list[float], box_b: list[float]) -> float:
+ left = max(box_a[0], box_b[0])
+ top = max(box_a[1], box_b[1])
+ right = min(box_a[2], box_b[2])
+ bottom = min(box_a[3], box_b[3])
+ intersection = max(0.0, right - left) * max(0.0, bottom - top)
+ area_a = max(0.0, box_a[2] - box_a[0]) * max(0.0, box_a[3] - box_a[1])
+ area_b = max(0.0, box_b[2] - box_b[0]) * max(0.0, box_b[3] - box_b[1])
+ smaller_area = min(area_a, area_b)
+ return intersection / smaller_area if smaller_area else 0.0
+
+
+def serializable_vehicle_detections(detections: list[dict[str, Any]]) -> list[dict[str, Any]]:
+ vehicles: list[dict[str, Any]] = []
+ for detection in sorted(detections, key=lambda item: float(item["score"]), reverse=True):
+ if int(detection["label"]) != VEHICLE_CLASS_ID:
+ continue
+ box = [round(float(value), 2) for value in detection["box"]]
+ if any(
+ intersection_over_smaller(box, existing["bbox_xyxy"]) >= CONTAINMENT_THRESHOLD
+ for existing in vehicles
+ ):
+ continue
+ vehicles.append(
+ {
+ "class_id": VEHICLE_CLASS_ID,
+ "class_name": "vehicle",
+ "confidence": round(float(detection["score"]), 4),
+ "bbox_xyxy": box,
+ }
+ )
+ return vehicles
+
+
+def draw_detections(image_path: Path, detections: list[dict[str, Any]], output_path: Path) -> None:
+ import cv2
+ import numpy as np
+
+ canvas = cv2.cvtColor(np.asarray(Image.open(image_path).convert("RGB")), cv2.COLOR_RGB2BGR)
+ for detection in detections:
+ x1, y1, x2, y2 = [int(round(value)) for value in detection["bbox_xyxy"]]
+ label = f"vehicle {detection['confidence']:.2f}"
+ cv2.rectangle(canvas, (x1, y1), (x2, y2), (0, 140, 255), 4)
+ cv2.putText(
+ canvas,
+ label,
+ (x1, max(30, y1 - 8)),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 1.0,
+ (0, 140, 255),
+ 2,
+ )
+ cv2.imwrite(str(output_path), canvas)
+
+
+def main() -> int:
+ args = parse_args()
+ input_path = args.input.resolve()
+ output_dir = args.output.resolve()
+ annotated_dir = output_dir / "annotated"
+ raster_dir = output_dir / "rasters"
+ annotated_dir.mkdir(parents=True, exist_ok=True)
+ raster_dir.mkdir(parents=True, exist_ok=True)
+
+ cache_root = Path(tempfile.gettempdir()) / "geoai-object-detection"
+ os.environ.setdefault("MPLCONFIGDIR", str(cache_root / "matplotlib"))
+
+ if not input_path.exists():
+ print(f"Input does not exist: {input_path}", file=sys.stderr)
+ return 2
+
+ image_paths, skipped = load_image_paths(input_path)
+ if not image_paths:
+ print("No supported images found.", file=sys.stderr)
+ return 2
+
+ import geoai
+ import torch
+ from geoai.object_detect import NWPU_VHR10_CLASSES, multiclass_detection
+
+ started = time.perf_counter()
+ results: list[dict[str, Any]] = []
+ for image_path in image_paths:
+ raster_path = raster_dir / f"{image_path.stem}_instances.tif"
+ _, inference_seconds, raw_detections = multiclass_detection(
+ input_path=str(image_path),
+ output_path=str(raster_path),
+ window_size=args.window_size,
+ overlap=args.overlap,
+ confidence_threshold=args.confidence,
+ nms_threshold=args.nms_threshold,
+ batch_size=args.batch_size,
+ device=torch.device("cpu"),
+ )
+ vehicles = serializable_vehicle_detections(raw_detections)
+ annotated_path = annotated_dir / f"{image_path.stem}.jpg"
+ draw_detections(image_path, vehicles, annotated_path)
+ with Image.open(image_path) as image:
+ width, height = image.size
+ raw_counts = Counter(NWPU_VHR10_CLASSES[int(item["label"])] for item in raw_detections)
+ results.append(
+ {
+ "file": image_path.name,
+ "width": width,
+ "height": height,
+ "annotated_file": str(annotated_path.relative_to(output_dir)),
+ "instance_raster": str(raster_path.relative_to(output_dir)),
+ "inference_seconds": round(float(inference_seconds), 3),
+ "raw_class_counts": dict(raw_counts),
+ "detections": vehicles,
+ }
+ )
+ del raw_detections
+
+ elapsed = round(time.perf_counter() - started, 3)
+ (output_dir / "detections.json").write_text(
+ json.dumps({"images": results}, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ metadata = {
+ "created_at": datetime.now(UTC).isoformat(),
+ "geoai_package": getattr(geoai, "__version__", "unknown"),
+ "device": "cpu",
+ "model": "giswqs/nwpu-vhr10-maskrcnn:best_model.pth",
+ "model_architecture": "Mask R-CNN ResNet-50 FPN",
+ "source_classes": NWPU_VHR10_CLASSES,
+ "kept_classes": ["vehicle"],
+ "confidence": args.confidence,
+ "window_size": args.window_size,
+ "overlap": args.overlap,
+ "nms_threshold": args.nms_threshold,
+ "containment_threshold": CONTAINMENT_THRESHOLD,
+ "batch_size": args.batch_size,
+ "processed_images": len(results),
+ "skipped_images": skipped,
+ "vehicle_count": sum(len(item["detections"]) for item in results),
+ "elapsed_seconds": elapsed,
+ "notes": [
+ "The NWPU-VHR10 vehicle class combines vehicle subtypes.",
+ "The model does not contain person or tree classes.",
+ "JPEG detections use pixel coordinates, not geographic coordinates.",
+ ],
+ }
+ (output_dir / "run_metadata.json").write_text(
+ json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ print(
+ f"Processed {len(results)} images; vehicles={metadata['vehicle_count']}; "
+ f"elapsed={elapsed}s"
+ )
+ print(f"Annotated images: {annotated_dir}")
+ print(f"JSON: {output_dir / 'detections.json'}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/capabilities/15-trajectory-analysis/README.md b/capabilities/15-trajectory-analysis/README.md
index 708e568..a206d9a 100644
--- a/capabilities/15-trajectory-analysis/README.md
+++ b/capabilities/15-trajectory-analysis/README.md
@@ -1,6 +1,120 @@
-# Trajectory Analysis
+# 轨迹分析与行为识别
-- 输入：带时间戳的无人机、车辆、人员或船舶轨迹。
-- 输出：偏航、越界、聚集、停留和轨迹类别。
-- 首个 Demo：规则检测加轨迹聚类。
+状态：首个 CPU Demo 已验证（2026-08-14）。
+## 能力边界
+
+当前 Demo 归为 **C（产品业务能力）**，不是 `geoai-py` 内置功能：
+
+- 产品实现用 GeoPandas、Shapely、PyProj 和 scikit-learn 完成轨迹度量、空间关系计算、DBSCAN 分组和规则判断。仅使用这些生态库、但没有实际调用 `geoai-py`，不能标为 B。
+- `geoai-py` 的准确角色是上游来源之一：其目标检测和地理结果导出可为跟踪系统提供观测，但 `geoai-py 0.42.0` 没有轨迹、目标跟踪或行为识别 API。本 Demo 运行时因此不安装或调用 `geoai-py`。
+
+未来若把 `geoai-py` 检测/导出、独立多目标跟踪器和本规则服务连成完整流水线，流水线可同时包含 A、B、C；当前已实现并验证的部分仍是 C。
+
+首版所称“行为识别”是可解释的时空规则，不是视频动作识别模型，也不负责把逐帧检测框关联成轨迹 ID。
+
+## Demo 契约
+
+输入是一个 `*.case.json` 清单，引用三类真实业务可导出的文件：
+
+1. 轨迹点 CSV：固定字段 `track_id,entity_type,timestamp,longitude,latitude`。
+2. 参考路线 GeoJSON：每条 `LineString` 带 `track_id`。
+3. 区域 GeoJSON：每个面带 `zone_id` 和 `zone_type`；首版识别 `restricted`。
+
+首版只接受带有效 CRS 的 WGS84 经纬度（`EPSG:4326`）。每条轨迹至少需要两个不同时间戳；同一轨迹的重复时间戳保留最后一条并在元数据中计数。
+
+仓库没有用户实测轨迹，因此 [generate_demo_inputs.py](./generate_demo_inputs.py) 会生成两组小型、可审计的合成代表样本：
+
+- `normal`：2 条连续移动且贴合参考路线的轨迹，预期无事件。
+- `difficult`：3 条乱序轨迹，含 1 条重复观测，以及可人工核对的停留、偏航、禁入区和双人聚集。
+
+生成数据位于 `shared/data/raw/15-trajectory-analysis/`，不会提交 Git。真实验证仍需用户提供同结构的脱敏轨迹。
+
+## 环境与运行
+
+```powershell
+# Python 3.12 隔离环境
+powershell -NoProfile -ExecutionPolicy Bypass -File .\scripts\setup.ps1 -Capability 15-trajectory-analysis
+
+$py = '.\.venvs\15-trajectory-analysis\Scripts\python.exe'
+
+# 第一次生成验收输入
+& $py .\capabilities\15-trajectory-analysis\generate_demo_inputs.py
+
+# 分析目录内的 normal.case.json 和 difficult.case.json
+& $py .\capabilities\15-trajectory-analysis\run_trajectory_analysis.py
+
+# 只分析一个用户清单；输出仍放在 <output>/<case_id>/
+& $py .\capabilities\15-trajectory-analysis\run_trajectory_analysis.py `
+ --input .\path\to\my.case.json `
+ --output .\shared\outputs\15-trajectory-analysis\my-run
+```
+
+脚本默认拒绝覆盖已有输入和结果。只有明确要替换此前运行时才传 `--overwrite`。
+
+## 规则与输出
+
+默认规则：
+
+| 事件 | 条件 |
+| --- | --- |
+| 停留 `stop` | 速度不高于 0.8 m/s，持续至少 60 秒 |
+| 偏航 `route_deviation` | 到参考路线距离大于 25 m，持续至少 20 秒 |
+| 禁入区 `restricted_zone` | 轨迹点落入 `restricted` 面 |
+| 聚集 `gathering` | 至少 2 条轨迹在 20 m 内，持续至少 30 秒 |
+
+清单可用 `thresholds` 对象覆盖上述阈值。相邻观测超过 60 秒时事件连续段会断开，避免把长时间数据缺失误算为持续行为。
+
+每个案例固定输出：
+
+| 文件 | 内容 |
+| --- | --- |
+| `trajectory_summary.csv` | 每条轨迹的时间、距离、速度、偏航比例、停留/禁入时长、聚类 ID 和行为标签 |
+| `events.json` | 固定事件字段、参与轨迹、起止时间、位置和规则细节 |
+| `trajectories.geojson` | WGS84 轨迹线及汇总属性 |
+| `events.geojson` | WGS84 事件点及事件属性 |
+| `analysis.png` | 轨迹、参考路线、区域和不同形状事件标记 |
+| `run_metadata.json` | Python/依赖版本、规则/模型、阈值、CPU、输入数、耗时、清洗数和限制 |
+
+DBSCAN 只对轨迹汇总特征做探索性分组；`cluster_id=-1` 表示没有足够相似轨迹形成簇，不代表异常已被确认。
+
+## 已测验收结果
+
+运行环境：Python 3.12.10、CPU；`pip check` 通过。目录级运行处理 2 个案例、5 条轨迹和 78 条去重后观测，总耗时约 2.8 秒。
+
+| 验收项 | 预期 | 实测 |
+| --- | --- | --- |
+| 正常样本 | 2 条轨迹，0 个事件 | 通过：2 条轨迹，0 个事件 |
+| 困难样本清洗 | 乱序可排序，删除 1 条重复时间观测 | 通过：删除 1 条 |
+| 困难样本行为 | 四类事件各至少 1 个 | 通过：停留、偏航、禁入区、聚集各 1 个 |
+| 失败处理 | 缺少必填 CSV 字段时退出码为 2，并指出字段 | 通过 |
+| 输出完整性 | 每个案例生成 6 个非空文件 | 通过 |
+| 视觉检查 | 正常/困难 PNG 非空，轨迹、区域和事件可区分 | 通过；两张图均为 1400 x 980 |
+
+自动验收：
+
+```powershell
+$py = '.\.venvs\15-trajectory-analysis\Scripts\python.exe'
+& $py -m unittest discover -s .\capabilities\15-trajectory-analysis\tests -v
+```
+
+## 许可证
+
+| 项目 | 当前记录 |
+| --- | --- |
+| `geoai-py 0.42.0` | MIT；仅为可选上游，本 Demo 环境未安装 |
+| pandas / GeoPandas / Shapely / scikit-learn | BSD 3-Clause 系列许可证 |
+| PyProj | MIT；同时需遵守其随附 PROJ 数据/组件许可 |
+| Matplotlib | Matplotlib License；随附字体等资产有各自许可证 |
+| Demo 数据 | 由本仓库脚本生成，无第三方数据集或模型权重 |
+| 预训练模型/权重 | 无 |
+
+以上是开发阶段记录，不等于已完成产品商用法务审查。
+
+## 已知限制与下一步
+
+- 合成样本只能验证程序逻辑，不能证明真实场景准确率；目前没有可报告的真实误报率和漏报率。
+- 聚集要求时间戳对齐，GPS 漂移、采样频率和轨迹断点会直接影响结果。
+- 偏航依赖可信参考路线；禁入区依赖有效区域数据；不同人员、车辆、船舶和无人机需要分别标定阈值。
+- 下一步应先选 1 个正常、1 个困难的脱敏实测轨迹，人工标注事件，再评估误报、漏报和阈值，而不是直接跑大目录。
+- 若输入来自无人机视频，还需在此能力之前引入独立的多目标跟踪器，并验证 ID 切换问题。
diff --git a/capabilities/15-trajectory-analysis/generate_demo_inputs.py b/capabilities/15-trajectory-analysis/generate_demo_inputs.py
new file mode 100644
index 0000000..213b108
--- /dev/null
+++ b/capabilities/15-trajectory-analysis/generate_demo_inputs.py
@@ -0,0 +1,197 @@
+"""Generate small, auditable trajectory-analysis demo inputs."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import math
+import sys
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+from typing import Any
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[2]
+ parser = argparse.ArgumentParser(description="Generate trajectory demo inputs.")
+ parser.add_argument(
+ "--output",
+ type=Path,
+ default=root / "shared" / "data" / "raw" / "15-trajectory-analysis",
+ )
+ parser.add_argument("--overwrite", action="store_true")
+ return parser.parse_args()
+
+
+def feature_collection(features: list[dict[str, Any]]) -> dict[str, Any]:
+ return {"type": "FeatureCollection", "features": features}
+
+
+def line_feature(track_id: str, coordinates: list[list[float]]) -> dict[str, Any]:
+ return {
+ "type": "Feature",
+ "properties": {"track_id": track_id},
+ "geometry": {"type": "LineString", "coordinates": coordinates},
+ }
+
+
+def write_json(path: Path, payload: dict[str, Any]) -> None:
+ path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
+
+
+def write_case(
+ output_dir: Path,
+ case_id: str,
+ description: str,
+ rows: list[dict[str, str]],
+ routes: list[dict[str, Any]],
+ zones: list[dict[str, Any]],
+) -> None:
+ csv_name = f"{case_id}_observations.csv"
+ routes_name = f"{case_id}_routes.geojson"
+ zones_name = f"{case_id}_zones.geojson"
+ with (output_dir / csv_name).open("w", newline="", encoding="utf-8") as stream:
+ writer = csv.DictWriter(
+ stream,
+ fieldnames=["track_id", "entity_type", "timestamp", "longitude", "latitude"],
+ )
+ writer.writeheader()
+ writer.writerows(rows)
+ write_json(output_dir / routes_name, feature_collection(routes))
+ write_json(output_dir / zones_name, feature_collection(zones))
+ write_json(
+ output_dir / f"{case_id}.case.json",
+ {
+ "case_id": case_id,
+ "description": description,
+ "crs": "EPSG:4326",
+ "observations": csv_name,
+ "reference_routes": routes_name,
+ "zones": zones_name,
+ },
+ )
+
+
+def observation(
+ track_id: str,
+ entity_type: str,
+ timestamp: datetime,
+ longitude: float,
+ latitude: float,
+) -> dict[str, str]:
+ return {
+ "track_id": track_id,
+ "entity_type": entity_type,
+ "timestamp": timestamp.isoformat().replace("+00:00", "Z"),
+ "longitude": f"{longitude:.7f}",
+ "latitude": f"{latitude:.7f}",
+ }
+
+
+def build_normal_case() -> tuple[list[dict[str, str]], list[dict[str, Any]]]:
+ started = datetime(2026, 8, 14, 1, 0, tzinfo=UTC)
+ rows: list[dict[str, str]] = []
+ routes: list[dict[str, Any]] = []
+ for track_id, entity_type, base_lat in (
+ ("drone-normal-01", "drone", 31.2000),
+ ("vehicle-normal-01", "vehicle", 31.1995),
+ ):
+ coordinates: list[list[float]] = []
+ for index in range(16):
+ lon = 121.4700 + index * 0.00012
+ lat = base_lat + math.sin(index / 2) * 0.000006
+ coordinates.append([lon, base_lat])
+ rows.append(observation(track_id, entity_type, started + timedelta(seconds=10 * index), lon, lat))
+ routes.append(line_feature(track_id, coordinates))
+ return rows, routes
+
+
+def build_difficult_case() -> tuple[list[dict[str, str]], list[dict[str, Any]]]:
+ started = datetime(2026, 8, 14, 2, 0, tzinfo=UTC)
+ rows: list[dict[str, str]] = []
+ routes: list[dict[str, Any]] = []
+
+ car_route = [[121.4700 + index * 0.00010, 31.2000] for index in range(20)]
+ routes.append(line_feature("vehicle-difficult-01", car_route))
+ for index in range(20):
+ if index <= 5:
+ lon, lat = 121.4700 + index * 0.00010, 31.2000
+ elif index <= 13:
+ lon, lat = 121.4705, 31.2000
+ else:
+ lon, lat = 121.4705 + (index - 13) * 0.00010, 31.20055
+ rows.append(
+ observation(
+ "vehicle-difficult-01", "vehicle", started + timedelta(seconds=10 * index), lon, lat
+ )
+ )
+ # One duplicate is intentional: the analyzer must deduplicate it and report the cleanup.
+ rows.append(observation("vehicle-difficult-01", "vehicle", started + timedelta(seconds=100), 121.4705, 31.2000))
+
+ for track_id, base_lat, offset in (
+ ("person-group-01", 31.19935, -0.000035),
+ ("person-group-02", 31.19965, 0.000035),
+ ):
+ coordinates: list[list[float]] = []
+ for index in range(13):
+ lon = 121.4700 + index * 0.00011
+ if 4 <= index <= 9:
+ lat = 31.19950 + offset
+ else:
+ lat = base_lat
+ coordinates.append([lon, lat])
+ rows.append(observation(track_id, "person", started + timedelta(seconds=10 * index), lon, lat))
+ routes.append(line_feature(track_id, coordinates))
+
+ # Deliberately reverse the rows so sorting is exercised on the difficult case.
+ rows.reverse()
+ return rows, routes
+
+
+def main() -> int:
+ args = parse_args()
+ output_dir = args.output.resolve()
+ expected = output_dir / "normal.case.json"
+ if expected.exists() and not args.overwrite:
+ print(f"Demo inputs already exist: {output_dir}. Use --overwrite to replace them.", file=sys.stderr)
+ return 2
+ output_dir.mkdir(parents=True, exist_ok=True)
+ zone = {
+ "type": "Feature",
+ "properties": {"zone_id": "restricted-01", "zone_type": "restricted"},
+ "geometry": {
+ "type": "Polygon",
+ "coordinates": [[
+ [121.47035, 31.20030],
+ [121.47125, 31.20030],
+ [121.47125, 31.20080],
+ [121.47035, 31.20080],
+ [121.47035, 31.20030],
+ ]],
+ },
+ }
+ normal_rows, normal_routes = build_normal_case()
+ write_case(
+ output_dir,
+ "normal",
+ "Two continuously moving tracks following their routes without rule events.",
+ normal_rows,
+ normal_routes,
+ [zone],
+ )
+ difficult_rows, difficult_routes = build_difficult_case()
+ write_case(
+ output_dir,
+ "difficult",
+ "Unsorted observations with a duplicate, a stop, route deviation, restricted-zone entry and gathering.",
+ difficult_rows,
+ difficult_routes,
+ [zone],
+ )
+ print(f"Generated normal and difficult demo inputs in {output_dir}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/capabilities/15-trajectory-analysis/requirements.txt b/capabilities/15-trajectory-analysis/requirements.txt
index 86dd1e1..a989bce 100644
--- a/capabilities/15-trajectory-analysis/requirements.txt
+++ b/capabilities/15-trajectory-analysis/requirements.txt
@@ -1,5 +1,4 @@
-r ../../requirements/base.txt
geopandas>=1,<2
scikit-learn>=1.5,<2
-movingpandas>=0.19,<1
-
+matplotlib>=3.9,<4
diff --git a/capabilities/15-trajectory-analysis/run_trajectory_analysis.py b/capabilities/15-trajectory-analysis/run_trajectory_analysis.py
new file mode 100644
index 0000000..947c0f8
--- /dev/null
+++ b/capabilities/15-trajectory-analysis/run_trajectory_analysis.py
@@ -0,0 +1,636 @@
+"""Analyze timestamped WGS84 trajectories with clustering and explicit rules."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import platform
+import sys
+import time
+from collections import defaultdict
+from datetime import UTC, datetime
+from importlib.metadata import PackageNotFoundError, version
+from pathlib import Path
+from typing import Any, Iterable
+
+import geopandas as gpd
+import matplotlib
+import numpy as np
+import pandas as pd
+from pyproj import CRS
+from shapely.geometry import LineString, Point, mapping
+from sklearn.cluster import DBSCAN
+from sklearn.preprocessing import StandardScaler
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+
+DEFAULT_THRESHOLDS = {
+ "stop_speed_mps": 0.8,
+ "stop_duration_seconds": 60.0,
+ "route_deviation_m": 25.0,
+ "route_deviation_duration_seconds": 20.0,
+ "gathering_radius_m": 20.0,
+ "gathering_duration_seconds": 30.0,
+ "max_observation_gap_seconds": 60.0,
+}
+REQUIRED_COLUMNS = {"track_id", "entity_type", "timestamp", "longitude", "latitude"}
+EVENT_COLORS = {
+ "stop": "#d55e00",
+ "route_deviation": "#cc79a7",
+ "restricted_zone": "#e69f00",
+ "gathering": "#009e73",
+}
+EVENT_MARKERS = {
+ "stop": "s",
+ "route_deviation": "^",
+ "restricted_zone": "D",
+ "gathering": "P",
+}
+EVENT_SIZES = {
+ "stop": 62,
+ "route_deviation": 42,
+ "restricted_zone": 92,
+ "gathering": 68,
+}
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[2]
+ parser = argparse.ArgumentParser(description="Analyze timestamped WGS84 trajectories.")
+ parser.add_argument(
+ "--input",
+ type=Path,
+ default=root / "shared" / "data" / "raw" / "15-trajectory-analysis",
+ help="A .case.json manifest or a directory containing manifests.",
+ )
+ parser.add_argument(
+ "--output",
+ type=Path,
+ default=root / "shared" / "outputs" / "15-trajectory-analysis",
+ )
+ parser.add_argument("--overwrite", action="store_true", help="Replace existing case outputs.")
+ return parser.parse_args()
+
+
+def package_version(name: str) -> str:
+ try:
+ return version(name)
+ except PackageNotFoundError:
+ return "not-installed"
+
+
+def utc_text(value: pd.Timestamp | datetime) -> str:
+ return value.to_pydatetime().astimezone(UTC).isoformat() if isinstance(value, pd.Timestamp) else value.astimezone(UTC).isoformat()
+
+
+def load_json(path: Path) -> dict[str, Any]:
+ with path.open(encoding="utf-8") as stream:
+ payload = json.load(stream)
+ if not isinstance(payload, dict):
+ raise ValueError(f"JSON root must be an object: {path}")
+ return payload
+
+
+def resolve_input_path(manifest_path: Path, value: str) -> Path:
+ path = Path(value)
+ return path.resolve() if path.is_absolute() else (manifest_path.parent / path).resolve()
+
+
+def choose_metric_crs(longitude: float, latitude: float) -> CRS:
+ zone = int((longitude + 180) // 6) + 1
+ epsg = (32600 if latitude >= 0 else 32700) + zone
+ return CRS.from_epsg(epsg)
+
+
+def load_case(manifest_path: Path) -> tuple[dict[str, Any], pd.DataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame, int, CRS]:
+ manifest = load_json(manifest_path)
+ for field in ("case_id", "crs", "observations", "reference_routes", "zones"):
+ if not manifest.get(field):
+ raise ValueError(f"Manifest is missing '{field}': {manifest_path}")
+ if manifest["crs"] != "EPSG:4326":
+ raise ValueError("Demo v1 accepts only EPSG:4326 longitude/latitude observations.")
+ observations_path = resolve_input_path(manifest_path, manifest["observations"])
+ routes_path = resolve_input_path(manifest_path, manifest["reference_routes"])
+ zones_path = resolve_input_path(manifest_path, manifest["zones"])
+ frame = pd.read_csv(observations_path)
+ missing = REQUIRED_COLUMNS - set(frame.columns)
+ if missing:
+ raise ValueError(f"Observation CSV is missing columns: {', '.join(sorted(missing))}")
+ raw_count = len(frame)
+ if raw_count == 0:
+ raise ValueError("Observation CSV is empty.")
+ frame["track_id"] = frame["track_id"].astype(str).str.strip()
+ frame["entity_type"] = frame["entity_type"].astype(str).str.strip()
+ if (frame["track_id"] == "").any() or (frame["entity_type"] == "").any():
+ raise ValueError("track_id and entity_type cannot be empty.")
+ frame["timestamp"] = pd.to_datetime(frame["timestamp"], utc=True, errors="raise")
+ frame["longitude"] = pd.to_numeric(frame["longitude"], errors="raise")
+ frame["latitude"] = pd.to_numeric(frame["latitude"], errors="raise")
+ valid = frame["longitude"].between(-180, 180) & frame["latitude"].between(-90, 90)
+ if not valid.all():
+ raise ValueError("longitude/latitude contains values outside WGS84 bounds.")
+ frame = (
+ frame.sort_values(["track_id", "timestamp"])
+ .drop_duplicates(["track_id", "timestamp"], keep="last")
+ .reset_index(drop=True)
+ )
+ duplicate_count = raw_count - len(frame)
+ if frame.groupby("track_id").size().min() < 2:
+ raise ValueError("Every track must contain at least two distinct timestamps.")
+ routes = gpd.read_file(routes_path)
+ zones = gpd.read_file(zones_path)
+ if "track_id" not in routes.columns:
+ raise ValueError("Reference route GeoJSON must contain a track_id property.")
+ if "zone_id" not in zones.columns or "zone_type" not in zones.columns:
+ raise ValueError("Zone GeoJSON must contain zone_id and zone_type properties.")
+ if routes.crs is None or zones.crs is None:
+ raise ValueError("Route and zone GeoJSON files must declare a CRS.")
+ expected = set(frame["track_id"])
+ missing_routes = expected - set(routes["track_id"].astype(str))
+ if missing_routes:
+ raise ValueError(f"Missing reference routes for: {', '.join(sorted(missing_routes))}")
+ metric_crs = choose_metric_crs(float(frame["longitude"].mean()), float(frame["latitude"].mean()))
+ return manifest, frame, routes.to_crs(metric_crs), zones.to_crs(metric_crs), duplicate_count, metric_crs
+
+
+def true_runs(flags: list[bool], times: list[pd.Timestamp], max_gap: float) -> Iterable[tuple[int, int]]:
+ start: int | None = None
+ for index, flag in enumerate(flags):
+ separated = index > 0 and (times[index] - times[index - 1]).total_seconds() > max_gap
+ if flag and (start is None or separated):
+ if start is not None:
+ yield start, index - 1
+ start = index
+ elif not flag and start is not None:
+ yield start, index - 1
+ start = None
+ if start is not None:
+ yield start, len(flags) - 1
+
+
+def make_event(
+ case_id: str,
+ event_type: str,
+ track_ids: list[str],
+ started: pd.Timestamp,
+ ended: pd.Timestamp,
+ point: Point,
+ details: dict[str, Any],
+) -> dict[str, Any]:
+ duration = max(0.0, (ended - started).total_seconds())
+ return {
+ "event_id": "",
+ "case_id": case_id,
+ "event_type": event_type,
+ "track_ids": track_ids,
+ "start_time": utc_text(started),
+ "end_time": utc_text(ended),
+ "duration_seconds": round(duration, 3),
+ "longitude": round(point.x, 7),
+ "latitude": round(point.y, 7),
+ "details": details,
+ }
+
+
+def analyze_tracks(
+ case_id: str,
+ frame: pd.DataFrame,
+ routes_metric: gpd.GeoDataFrame,
+ zones_metric: gpd.GeoDataFrame,
+ metric_crs: CRS,
+ thresholds: dict[str, float],
+) -> tuple[list[dict[str, Any]], list[dict[str, Any]], dict[str, gpd.GeoDataFrame]]:
+ points = gpd.GeoDataFrame(
+ frame.copy(),
+ geometry=gpd.points_from_xy(frame["longitude"], frame["latitude"]),
+ crs="EPSG:4326",
+ )
+ points_metric = points.to_crs(metric_crs)
+ route_map = {str(row.track_id): row.geometry for row in routes_metric.itertuples()}
+ restricted = zones_metric[zones_metric["zone_type"].astype(str) == "restricted"]
+ events: list[dict[str, Any]] = []
+ summaries: list[dict[str, Any]] = []
+ track_frames: dict[str, gpd.GeoDataFrame] = {}
+
+ for track_id, group_indexes in points_metric.groupby("track_id", sort=True).groups.items():
+ track = points_metric.loc[group_indexes].sort_values("timestamp").copy().reset_index(drop=True)
+ track_wgs84 = track.to_crs("EPSG:4326")
+ track_frames[str(track_id)] = track_wgs84
+ times = list(track["timestamp"])
+ step_dt = track["timestamp"].diff().dt.total_seconds().fillna(0.0).to_numpy()
+ step_distance = np.zeros(len(track), dtype=float)
+ for index in range(1, len(track)):
+ step_distance[index] = track.geometry.iloc[index - 1].distance(track.geometry.iloc[index])
+ step_speed = np.divide(step_distance, step_dt, out=np.zeros_like(step_distance), where=step_dt > 0)
+ max_gap = thresholds["max_observation_gap_seconds"]
+
+ slow_intervals = [
+ index > 0 and step_dt[index] <= max_gap and step_speed[index] <= thresholds["stop_speed_mps"]
+ for index in range(len(track))
+ ]
+ stop_seconds = 0.0
+ for start, end in true_runs(slow_intervals, times, max_gap):
+ event_start = max(0, start - 1)
+ duration = (times[end] - times[event_start]).total_seconds()
+ if duration >= thresholds["stop_duration_seconds"]:
+ stop_seconds += duration
+ point = track_wgs84.geometry.iloc[event_start : end + 1].union_all().centroid
+ events.append(
+ make_event(
+ case_id,
+ "stop",
+ [str(track_id)],
+ times[event_start],
+ times[end],
+ point,
+ {"max_speed_mps": round(float(step_speed[start : end + 1].max()), 3)},
+ )
+ )
+
+ route = route_map[str(track_id)]
+ route_distances = np.array([geometry.distance(route) for geometry in track.geometry], dtype=float)
+ deviated = list(route_distances > thresholds["route_deviation_m"])
+ deviation_seconds = 0.0
+ for start, end in true_runs(deviated, times, max_gap):
+ duration = (times[end] - times[start]).total_seconds()
+ if duration >= thresholds["route_deviation_duration_seconds"]:
+ deviation_seconds += duration
+ events.append(
+ make_event(
+ case_id,
+ "route_deviation",
+ [str(track_id)],
+ times[start],
+ times[end],
+ track_wgs84.geometry.iloc[end],
+ {"max_distance_m": round(float(route_distances[start : end + 1].max()), 3)},
+ )
+ )
+
+ restricted_seconds = 0.0
+ inside_by_zone: dict[str, list[bool]] = {}
+ for zone in restricted.itertuples():
+ flags = [bool(zone.geometry.covers(geometry)) for geometry in track.geometry]
+ inside_by_zone[str(zone.zone_id)] = flags
+ for start, end in true_runs(flags, times, max_gap):
+ duration = (times[end] - times[start]).total_seconds()
+ restricted_seconds += duration
+ events.append(
+ make_event(
+ case_id,
+ "restricted_zone",
+ [str(track_id)],
+ times[start],
+ times[end],
+ track_wgs84.geometry.iloc[end],
+ {"zone_id": str(zone.zone_id)},
+ )
+ )
+
+ duration_seconds = (times[-1] - times[0]).total_seconds()
+ summaries.append(
+ {
+ "case_id": case_id,
+ "track_id": str(track_id),
+ "entity_type": str(track["entity_type"].iloc[0]),
+ "point_count": len(track),
+ "start_time": utc_text(times[0]),
+ "end_time": utc_text(times[-1]),
+ "duration_seconds": round(duration_seconds, 3),
+ "distance_m": round(float(step_distance.sum()), 3),
+ "average_speed_mps": round(float(step_distance.sum() / duration_seconds), 3),
+ "max_speed_mps": round(float(step_speed.max()), 3),
+ "route_deviation_ratio": round(float(np.mean(deviated)), 4),
+ "restricted_zone_seconds": round(restricted_seconds, 3),
+ "stop_seconds": round(stop_seconds, 3),
+ "cluster_id": -1,
+ "behavior_labels": "",
+ }
+ )
+
+ gathering_events = detect_gathering(case_id, points_metric, thresholds)
+ events.extend(gathering_events)
+ labels_by_track: dict[str, set[str]] = defaultdict(set)
+ for event in events:
+ for track_id in event["track_ids"]:
+ labels_by_track[track_id].add(event["event_type"])
+
+ if len(summaries) >= 2:
+ features = np.array(
+ [
+ [
+ item["distance_m"],
+ item["duration_seconds"],
+ item["average_speed_mps"],
+ item["route_deviation_ratio"],
+ item["stop_seconds"] / max(item["duration_seconds"], 1),
+ ]
+ for item in summaries
+ ]
+ )
+ labels = DBSCAN(eps=1.35, min_samples=2).fit_predict(StandardScaler().fit_transform(features))
+ for item, label in zip(summaries, labels, strict=True):
+ item["cluster_id"] = int(label)
+ for item in summaries:
+ item["behavior_labels"] = "|".join(sorted(labels_by_track[item["track_id"]])) or "normal"
+
+ events.sort(key=lambda item: (item["start_time"], item["event_type"], item["track_ids"]))
+ for index, event in enumerate(events, start=1):
+ event["event_id"] = f"{case_id}-event-{index:03d}"
+ return summaries, events, track_frames
+
+
+def detect_gathering(
+ case_id: str, points_metric: gpd.GeoDataFrame, thresholds: dict[str, float]
+) -> list[dict[str, Any]]:
+ occurrences: dict[tuple[str, ...], list[tuple[pd.Timestamp, Point]]] = defaultdict(list)
+ radius = thresholds["gathering_radius_m"]
+ for timestamp, group in points_metric.groupby("timestamp"):
+ rows = list(group.itertuples())
+ adjacency: dict[str, set[str]] = {str(row.track_id): set() for row in rows}
+ geometries = {str(row.track_id): row.geometry for row in rows}
+ for left_index, left in enumerate(rows):
+ for right in rows[left_index + 1 :]:
+ if left.geometry.distance(right.geometry) <= radius:
+ adjacency[str(left.track_id)].add(str(right.track_id))
+ adjacency[str(right.track_id)].add(str(left.track_id))
+ remaining = set(adjacency)
+ while remaining:
+ seed = remaining.pop()
+ component = {seed}
+ queue = [seed]
+ while queue:
+ current = queue.pop()
+ neighbors = adjacency[current] & remaining
+ remaining -= neighbors
+ component |= neighbors
+ queue.extend(neighbors)
+ if len(component) >= 2:
+ members = tuple(sorted(component))
+ centroid = gpd.GeoSeries([geometries[item] for item in members], crs=points_metric.crs).union_all().centroid
+ occurrences[members].append((timestamp, centroid))
+
+ events: list[dict[str, Any]] = []
+ max_gap = thresholds["max_observation_gap_seconds"]
+ for members, values in occurrences.items():
+ values.sort(key=lambda item: item[0])
+ groups: list[list[tuple[pd.Timestamp, Point]]] = [[values[0]]]
+ for value in values[1:]:
+ if (value[0] - groups[-1][-1][0]).total_seconds() <= max_gap:
+ groups[-1].append(value)
+ else:
+ groups.append([value])
+ for run in groups:
+ duration = (run[-1][0] - run[0][0]).total_seconds()
+ if duration < thresholds["gathering_duration_seconds"]:
+ continue
+ wgs84_point = gpd.GeoSeries([run[-1][1]], crs=points_metric.crs).to_crs("EPSG:4326").iloc[0]
+ events.append(
+ make_event(
+ case_id,
+ "gathering",
+ list(members),
+ run[0][0],
+ run[-1][0],
+ wgs84_point,
+ {"member_count": len(members), "radius_m": radius},
+ )
+ )
+ return events
+
+
+def write_outputs(
+ output_dir: Path,
+ manifest: dict[str, Any],
+ summaries: list[dict[str, Any]],
+ events: list[dict[str, Any]],
+ tracks: dict[str, gpd.GeoDataFrame],
+ routes_wgs84: gpd.GeoDataFrame,
+ zones_wgs84: gpd.GeoDataFrame,
+ metadata: dict[str, Any],
+) -> None:
+ output_dir.mkdir(parents=True, exist_ok=True)
+ pd.DataFrame(summaries).to_csv(output_dir / "trajectory_summary.csv", index=False, encoding="utf-8-sig")
+ (output_dir / "events.json").write_text(
+ json.dumps({"case_id": manifest["case_id"], "events": events}, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+ trajectory_features = []
+ summary_map = {item["track_id"]: item for item in summaries}
+ for track_id, track in tracks.items():
+ properties = dict(summary_map[track_id])
+ trajectory_features.append(
+ {"type": "Feature", "properties": properties, "geometry": mapping(LineString(track.geometry.tolist()))}
+ )
+ write_geojson(output_dir / "trajectories.geojson", trajectory_features)
+ event_features = [
+ {
+ "type": "Feature",
+ "properties": {key: value for key, value in event.items() if key not in {"longitude", "latitude"}},
+ "geometry": {"type": "Point", "coordinates": [event["longitude"], event["latitude"]]},
+ }
+ for event in events
+ ]
+ write_geojson(output_dir / "events.geojson", event_features)
+ render_map(output_dir / "analysis.png", tracks, routes_wgs84, zones_wgs84, events, manifest["case_id"])
+ (output_dir / "run_metadata.json").write_text(
+ json.dumps(metadata, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+
+
+def write_geojson(path: Path, features: list[dict[str, Any]]) -> None:
+ path.write_text(
+ json.dumps(
+ {"type": "FeatureCollection", "name": path.stem, "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}}, "features": features},
+ ensure_ascii=False,
+ indent=2,
+ ),
+ encoding="utf-8",
+ )
+
+
+def render_map(
+ path: Path,
+ tracks: dict[str, gpd.GeoDataFrame],
+ routes: gpd.GeoDataFrame,
+ zones: gpd.GeoDataFrame,
+ events: list[dict[str, Any]],
+ case_id: str,
+) -> None:
+ figure, axis = plt.subplots(figsize=(10, 7), dpi=140)
+ zones.plot(ax=axis, facecolor="#f0e442", edgecolor="#8c6d1f", alpha=0.28, linewidth=1.5)
+ routes.plot(ax=axis, color="#666666", linestyle="--", linewidth=1.2)
+ palette = ["#0072b2", "#009e73", "#d55e00", "#cc79a7", "#56b4e9"]
+ for index, (track_id, track) in enumerate(sorted(tracks.items())):
+ color = palette[index % len(palette)]
+ axis.plot(track.geometry.x, track.geometry.y, color=color, linewidth=2.2, marker="o", markersize=2.8, label=track_id)
+ axis.annotate(track_id, (track.geometry.x.iloc[-1], track.geometry.y.iloc[-1]), fontsize=7, color=color)
+ for event in events:
+ event_type = event["event_type"]
+ axis.scatter(
+ [event["longitude"]],
+ [event["latitude"]],
+ marker=EVENT_MARKERS[event_type],
+ s=EVENT_SIZES[event_type],
+ color=EVENT_COLORS[event_type],
+ edgecolor="white",
+ linewidth=0.8,
+ zorder=5,
+ )
+ handles, labels = axis.get_legend_handles_labels()
+ present_event_types = {event["event_type"] for event in events}
+ handles.extend(
+ Line2D(
+ [0],
+ [0],
+ marker=EVENT_MARKERS[name],
+ color="none",
+ markerfacecolor=color,
+ markeredgecolor="white",
+ markersize=8,
+ label=name,
+ )
+ for name, color in EVENT_COLORS.items()
+ if name in present_event_types
+ )
+ labels.extend(name for name in EVENT_COLORS if name in present_event_types)
+ axis.legend(handles, labels, loc="best", fontsize=7, framealpha=0.9)
+ axis.set_title(f"Trajectory analysis: {case_id}")
+ axis.set_xlabel("Longitude (WGS84)")
+ axis.set_ylabel("Latitude (WGS84)")
+ axis.grid(alpha=0.18)
+ axis.ticklabel_format(useOffset=False)
+ figure.tight_layout()
+ figure.savefig(path)
+ plt.close(figure)
+
+
+def process_manifest(manifest_path: Path, output_dir: Path) -> dict[str, Any]:
+ started = time.perf_counter()
+ manifest, frame, routes_metric, zones_metric, duplicates, metric_crs = load_case(manifest_path)
+ thresholds = dict(DEFAULT_THRESHOLDS)
+ for key, value in manifest.get("thresholds", {}).items():
+ if key not in thresholds:
+ raise ValueError(f"Unsupported threshold: {key}")
+ thresholds[key] = float(value)
+ if thresholds[key] <= 0:
+ raise ValueError(f"Threshold must be greater than zero: {key}")
+ summaries, events, tracks = analyze_tracks(
+ str(manifest["case_id"]), frame, routes_metric, zones_metric, metric_crs, thresholds
+ )
+ elapsed = round(time.perf_counter() - started, 3)
+ metadata = {
+ "created_at": datetime.now(UTC).isoformat(),
+ "case_id": manifest["case_id"],
+ "input_manifest": str(manifest_path.resolve()),
+ "input_count": int(len(frame)),
+ "track_count": len(summaries),
+ "dropped_duplicate_observations": duplicates,
+ "event_count": len(events),
+ "event_counts": {name: sum(event["event_type"] == name for event in events) for name in EVENT_COLORS},
+ "elapsed_seconds": elapsed,
+ "device": "cpu",
+ "python": platform.python_version(),
+ "packages": {
+ "geoai-py": package_version("geoai-py"),
+ "pandas": package_version("pandas"),
+ "geopandas": package_version("geopandas"),
+ "shapely": package_version("shapely"),
+ "pyproj": package_version("pyproj"),
+ "scikit-learn": package_version("scikit-learn"),
+ "matplotlib": package_version("matplotlib"),
+ },
+ "model": {
+ "behavior_recognition": "deterministic-threshold-rules-v1",
+ "trajectory_grouping": "DBSCAN on standardized summary features",
+ "pretrained_weights": None,
+ },
+ "thresholds": thresholds,
+ "output_crs": "EPSG:4326",
+ "metric_crs": metric_crs.to_string(),
+ "limitations": [
+ "This is rule-based behavior detection, not learned action recognition.",
+ "Track identities must already be present; this demo does not associate detections across video frames.",
+ "Gathering requires aligned timestamps and is sensitive to sampling gaps and GPS error.",
+ "Thresholds are illustrative and require domain validation before operational use.",
+ ],
+ }
+ write_outputs(
+ output_dir,
+ manifest,
+ summaries,
+ events,
+ tracks,
+ routes_metric.to_crs("EPSG:4326"),
+ zones_metric.to_crs("EPSG:4326"),
+ metadata,
+ )
+ return metadata
+
+
+def main() -> int:
+ args = parse_args()
+ input_path = args.input.resolve()
+ output_root = args.output.resolve()
+ if not input_path.exists():
+ print(f"Input does not exist: {input_path}", file=sys.stderr)
+ return 2
+ manifests = [input_path] if input_path.is_file() else sorted(input_path.glob("*.case.json"))
+ if not manifests:
+ print(f"No .case.json manifests found: {input_path}", file=sys.stderr)
+ return 2
+ try:
+ cases = [(path, str(load_json(path).get("case_id", ""))) for path in manifests]
+ if any(not case_id for _, case_id in cases):
+ raise ValueError("Every manifest must define a non-empty case_id.")
+ destinations = [output_root / case_id for _, case_id in cases]
+ existing = [path for path in destinations if path.exists()]
+ if existing and not args.overwrite:
+ raise ValueError(f"Output already exists: {existing[0]}. Use --overwrite to replace it.")
+ results = [process_manifest(path, destination) for (path, _), destination in zip(cases, destinations, strict=True)]
+ aggregate = {
+ "created_at": datetime.now(UTC).isoformat(),
+ "input": str(input_path),
+ "input_count": sum(item["input_count"] for item in results),
+ "case_count": len(results),
+ "track_count": sum(item["track_count"] for item in results),
+ "dropped_duplicate_observations": sum(
+ item["dropped_duplicate_observations"] for item in results
+ ),
+ "event_count": sum(item["event_count"] for item in results),
+ "event_counts": {
+ name: sum(item["event_counts"][name] for item in results) for name in EVENT_COLORS
+ },
+ "elapsed_seconds": round(sum(item["elapsed_seconds"] for item in results), 3),
+ "device": "cpu",
+ "python": platform.python_version(),
+ "packages": results[0]["packages"],
+ "model": results[0]["model"],
+ "thresholds_by_case": {
+ item["case_id"]: item["thresholds"] for item in results
+ },
+ "limitations": results[0]["limitations"],
+ "cases": [
+ {"case_id": item["case_id"], "output": item["case_id"]} for item in results
+ ],
+ }
+ output_root.mkdir(parents=True, exist_ok=True)
+ (output_root / "run_metadata.json").write_text(
+ json.dumps(aggregate, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ except (OSError, ValueError, json.JSONDecodeError, pd.errors.ParserError) as exc:
+ print(f"Analysis failed: {exc}", file=sys.stderr)
+ return 2
+ print(
+ f"Processed {len(results)} case(s), {aggregate['track_count']} tracks and "
+ f"{aggregate['event_count']} events in {aggregate['elapsed_seconds']}s"
+ )
+ print(f"Outputs: {output_root}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/capabilities/15-trajectory-analysis/tests/test_demo.py b/capabilities/15-trajectory-analysis/tests/test_demo.py
new file mode 100644
index 0000000..a71fa40
--- /dev/null
+++ b/capabilities/15-trajectory-analysis/tests/test_demo.py
@@ -0,0 +1,73 @@
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+
+CAPABILITY_DIR = Path(__file__).resolve().parents[1]
+GENERATOR = CAPABILITY_DIR / "generate_demo_inputs.py"
+ANALYZER = CAPABILITY_DIR / "run_trajectory_analysis.py"
+
+
+class TrajectoryDemoTests(unittest.TestCase):
+ def run_command(self, *arguments: str) -> subprocess.CompletedProcess[str]:
+ return subprocess.run(
+ [sys.executable, *arguments],
+ text=True,
+ capture_output=True,
+ encoding="utf-8",
+ check=False,
+ )
+
+ def test_normal_and_difficult_cases(self) -> None:
+ with tempfile.TemporaryDirectory() as temporary:
+ root = Path(temporary)
+ inputs = root / "inputs"
+ outputs = root / "outputs"
+ generated = self.run_command(str(GENERATOR), "--output", str(inputs))
+ self.assertEqual(generated.returncode, 0, generated.stderr)
+ analyzed = self.run_command(str(ANALYZER), "--input", str(inputs), "--output", str(outputs))
+ self.assertEqual(analyzed.returncode, 0, analyzed.stderr)
+
+ normal = json.loads((outputs / "normal" / "run_metadata.json").read_text(encoding="utf-8"))
+ difficult = json.loads((outputs / "difficult" / "run_metadata.json").read_text(encoding="utf-8"))
+ self.assertEqual(normal["track_count"], 2)
+ self.assertEqual(normal["event_count"], 0)
+ self.assertEqual(difficult["dropped_duplicate_observations"], 1)
+ for event_type in ("stop", "route_deviation", "restricted_zone", "gathering"):
+ self.assertGreaterEqual(difficult["event_counts"][event_type], 1)
+ for case_id in ("normal", "difficult"):
+ for filename in (
+ "trajectory_summary.csv",
+ "events.json",
+ "trajectories.geojson",
+ "events.geojson",
+ "analysis.png",
+ "run_metadata.json",
+ ):
+ self.assertGreater((outputs / case_id / filename).stat().st_size, 0)
+
+ def test_missing_required_column_fails_cleanly(self) -> None:
+ with tempfile.TemporaryDirectory() as temporary:
+ root = Path(temporary)
+ inputs = root / "inputs"
+ self.assertEqual(self.run_command(str(GENERATOR), "--output", str(inputs)).returncode, 0)
+ csv_path = inputs / "normal_observations.csv"
+ csv_path.write_text("track_id,timestamp,longitude,latitude\na,2026-01-01T00:00:00Z,1,1\n", encoding="utf-8")
+ result = self.run_command(
+ str(ANALYZER),
+ "--input",
+ str(inputs / "normal.case.json"),
+ "--output",
+ str(root / "outputs"),
+ )
+ self.assertEqual(result.returncode, 2)
+ self.assertIn("entity_type", result.stderr)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/capabilities/README.md b/capabilities/README.md
index 97afea5..78d6cfd 100644
--- a/capabilities/README.md
+++ b/capabilities/README.md
@@ -1,6 +1,23 @@
# Capabilities
-目录编号表示建议研究顺序，不代表运行时依赖关系。
+目录编号表示建议研究顺序，不代表运行时依赖关系，也不代表每个目录都是
+`opengeos/geoai` 内置能力。
+
+## 基于 opengeos/geoai 的能力分层
+
+| 层级 | 含义 | 当前工作区示例 |
+| --- | --- | --- |
+| A：直接能力 | `geoai-py` 已有模块或文档直接覆盖 | 目标检测、分割、分类、变化检测、影像处理/导出 |
+| B：生态组合 | 以 `geoai-py` 为核心，再组合地理计算或业务库 | 空间测量、成果质检、灾害统计、部分三维分析 |
+| C：产品业务能力 | 主要由现有产品后端、规则和数据服务实现 | 风险规则、航线规划、集群调度、GeoLLM、知识图谱、模型治理 |
+
+判断一个方向是否属于 GeoAI 项目，要看它是否处理地理空间数据并调用 AI 或
+空间分析；判断它是否是 `geoai-py` 的能力，则必须以该仓库当前源码和文档为准。
+
+官方依据：
+
+- https://github.com/opengeos/geoai
+- https://pypi.org/project/geoai-py/
每个能力目录至少包含：
@@ -8,4 +25,3 @@
- `requirements.txt`: 公共依赖之外的专属依赖。
开始实现时再增加 `src/`、`tests/`、`examples/` 和 `configs/`，避免初期产生大量空目录。
-
diff --git a/scripts/check-environment.ps1 b/scripts/check-environment.ps1
index 1094daf..93b5f46 100644
--- a/scripts/check-environment.ps1
+++ b/scripts/check-environment.ps1
@@ -6,6 +6,14 @@
Write-Host "Python installations:"
py -0p
+$PythonCandidates = @(
+ (Join-Path $env:LOCALAPPDATA "Programs/Python/Python312/python.exe"),
+ (Join-Path $env:LOCALAPPDATA "Programs/Python/Python311/python.exe"),
+ (Join-Path $env:ProgramFiles "Python312/python.exe"),
+ (Join-Path $env:ProgramFiles "Python311/python.exe")
+)
+$SupportedPython = $PythonCandidates | Where-Object { Test-Path $_ } | Select-Object -First 1
+
Write-Host ""
Write-Host "Node:"
node --version
@@ -18,7 +26,8 @@
}
Write-Host ""
-if ((py -0p | Select-String "3.11|3.12")) {
+if ($SupportedPython) {
+ Write-Host "Detected: $(& $SupportedPython --version) at $SupportedPython"
Write-Host "Python version: ready" -ForegroundColor Green
} else {
Write-Warning "Install Python 3.11 or 3.12 before running setup.ps1."
diff --git a/scripts/serve_workbench_console.py b/scripts/serve_workbench_console.py
new file mode 100644
index 0000000..83126a9
--- /dev/null
+++ b/scripts/serve_workbench_console.py
@@ -0,0 +1,91 @@
+"""Serve the local GeoAI Workbench console from the repository root."""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import PurePosixPath
+from http import HTTPStatus
+from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from urllib.parse import unquote, urlsplit
+
+
+DEFAULT_HOST = "127.0.0.1"
+DEFAULT_PORT = 6173
+ALLOWED_PATH_PREFIXES = (
+ "apps/workbench-console",
+ "shared/outputs",
+ "shared/data/raw/01-object-detection",
+)
+
+
+class WorkbenchConsoleHandler(SimpleHTTPRequestHandler):
+ """Read-only static handler rooted at the workbench repository."""
+
+ def do_GET(self) -> None: # noqa: N802 - inherited standard-library method name
+ if urlsplit(self.path).path == "/":
+ self.send_response(HTTPStatus.FOUND)
+ self.send_header("Location", "/apps/workbench-console/")
+ self.end_headers()
+ return
+ super().do_GET()
+
+ def translate_path(self, path: str) -> str:
+ """Expose only the static UI and artifacts required by the local console."""
+ decoded_path = unquote(urlsplit(path).path).lstrip("/")
+ requested = PurePosixPath(decoded_path)
+ is_allowed = any(
+ decoded_path == prefix or decoded_path.startswith(f"{prefix}/")
+ for prefix in ALLOWED_PATH_PREFIXES
+ )
+ if ".." in requested.parts or not is_allowed:
+ return os.fspath(Path(self.directory) / ".console-forbidden")
+ if decoded_path == "apps/workbench-console" or decoded_path.startswith("apps/workbench-console/"):
+ console_relative = requested.parts[2:]
+ return os.fspath(Path(self.directory) / "apps" / "workbench-console" / "dist" / Path(*console_relative))
+ return os.fspath(Path(self.directory).joinpath(*requested.parts))
+
+ def end_headers(self) -> None:
+ self.send_header("Cache-Control", "no-store")
+ self.send_header("X-Content-Type-Options", "nosniff")
+ super().end_headers()
+
+
+def parse_args() -> argparse.Namespace:
+ root = Path(__file__).resolve().parents[1]
+ parser = argparse.ArgumentParser(description="Serve the local GeoAI Workbench console.")
+ parser.add_argument("--host", default=DEFAULT_HOST, help="Bind address. Defaults to loopback only.")
+ parser.add_argument("--port", type=int, default=DEFAULT_PORT, help="TCP port in the 6000-6999 range.")
+ parser.add_argument("--root", type=Path, default=root, help="Workbench repository root to serve.")
+ return parser.parse_args()
+
+
+def main() -> int:
+ args = parse_args()
+ if not 6000 <= args.port <= 6999:
+ raise SystemExit("Port must be in the 6000-6999 range.")
+ if args.host not in {"127.0.0.1", "localhost", "::1"}:
+ raise SystemExit("This console is local-only. Use 127.0.0.1, localhost, or ::1.")
+ root = args.root.resolve()
+ app_dir = root / "apps" / "workbench-console"
+ if not (root / "shared").is_dir() or not (app_dir / "dist" / "index.html").is_file():
+ raise SystemExit(f"Not a GeoAI Workbench root: {root}")
+
+ handler = lambda *handler_args, **handler_kwargs: WorkbenchConsoleHandler( # noqa: E731
+ *handler_args, directory=os.fspath(root), **handler_kwargs
+ )
+ server = ThreadingHTTPServer((args.host, args.port), handler)
+ print(f"GeoAI Workbench console: http://{args.host}:{args.port}")
+ print(f"Serving built console and read-only artifacts from: {root}")
+ try:
+ server.serve_forever()
+ except KeyboardInterrupt:
+ print("\nConsole stopped.")
+ finally:
+ server.server_close()
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/tests/test_serve_workbench_console.py b/tests/test_serve_workbench_console.py
new file mode 100644
index 0000000..d2bf1ad
--- /dev/null
+++ b/tests/test_serve_workbench_console.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import importlib.util
+import unittest
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[1]
+SCRIPT = ROOT / "scripts" / "serve_workbench_console.py"
+SPEC = importlib.util.spec_from_file_location("serve_workbench_console", SCRIPT)
+assert SPEC and SPEC.loader
+MODULE = importlib.util.module_from_spec(SPEC)
+SPEC.loader.exec_module(MODULE)
+
+
+class WorkbenchConsoleHandlerTests(unittest.TestCase):
+ def make_handler(self) -> MODULE.WorkbenchConsoleHandler:
+ handler = object.__new__(MODULE.WorkbenchConsoleHandler)
+ handler.directory = str(ROOT)
+ return handler
+
+ def test_allows_only_console_artifacts_and_detection_originals(self) -> None:
+ handler = self.make_handler()
+ self.assertEqual(
+ Path(handler.translate_path("/apps/workbench-console/index.html")),
+ ROOT / "apps" / "workbench-console" / "dist" / "index.html",
+ )
+ self.assertEqual(
+ Path(handler.translate_path("/shared/outputs/15-trajectory-analysis/run_metadata.json")),
+ ROOT / "shared" / "outputs" / "15-trajectory-analysis" / "run_metadata.json",
+ )
+ self.assertEqual(
+ Path(handler.translate_path("/shared/data/raw/01-object-detection/sample.jpeg")),
+ ROOT / "shared" / "data" / "raw" / "01-object-detection" / "sample.jpeg",
+ )
+
+ def test_blocks_repository_files_and_encoded_traversal(self) -> None:
+ handler = self.make_handler()
+ forbidden = ROOT / ".console-forbidden"
+ self.assertEqual(Path(handler.translate_path("/.git/HEAD")), forbidden)
+ self.assertEqual(Path(handler.translate_path("/%2e%2e/.env")), forbidden)
+ self.assertEqual(Path(handler.translate_path("/PROJECT_CONTEXT.md")), forbidden)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git "a/\346\226\260\345\257\271\350\257\235\345\220\257\345\212\250\350\257\264\346\230\216.txt" "b/\346\226\260\345\257\271\350\257\235\345\220\257\345\212\250\350\257\264\346\230\216.txt"
new file mode 100644
index 0000000..d7d20f2
--- /dev/null
+++ "b/\346\226\260\345\257\271\350\257\235\345\220\257\345\212\250\350\257\264\346\230\216.txt"
@@ -0,0 +1,24 @@
+GeoAI Workbench 新对话启动说明
+==============================
+
+1. 新建 Codex 对话。
+2. 把工作目录选择为：
+
+ E:\AllWorkProject\geoai-workbench
+
+3. 发送下面这句话，把“能力名称”替换成实际目标：
+
+ 使用 $geoai-capability-builder，创建下一个能力：<能力名称>。
+ 先读取 AGENTS.md 和 PROJECT_CONTEXT.md，再准备环境、Demo 输入输出和验收方式。
+
+示例：
+
+ 使用 $geoai-capability-builder，创建下一个能力：语义制图。
+ 先读取 AGENTS.md 和 PROJECT_CONTEXT.md，再准备环境、Demo 输入输出和验收方式。
+
+说明：
+
+- 不需要复制旧对话。
+- AGENTS.md 保存长期项目规则。
+- PROJECT_CONTEXT.md 保存当前进度、环境和实验结论。
+- 每完成一个能力，Codex 应更新 PROJECT_CONTEXT.md。
diff --git "a/\350\203\275\345\212\233\350\276\271\347\225\214\344\270\216\346\235\245\346\272\220\350\257\264\346\230\216.md" "b/\350\203\275\345\212\233\350\276\271\347\225\214\344\270\216\346\235\245\346\272\220\350\257\264\346\230\216.md"
new file mode 100644
index 0000000..5e698d6
--- /dev/null
+++ "b/\350\203\275\345\212\233\350\276\271\347\225\214\344\270\216\346\235\245\346\272\220\350\257\264\346\230\216.md"
@@ -0,0 +1,29 @@
+# GeoAI 能力边界与来源说明
+
+## 统一口径
+
+本项目所说的 GeoAI，默认指以 `opengeos/geoai` 开源项目（PyPI 包名
+`geoai-py`）为基础的地理空间人工智能工作流，而不是泛指所有空间智能、
+运筹优化或大语言模型功能。
+
+## 三层能力
+
+1. **直接能力**：`geoai-py` 已经提供相应模块、接口或示例，例如遥感影像
+ 目标检测、分割、分类、变化检测、影像切片、地理结果导出和可视化。
+2. **生态组合能力**：`geoai-py` 提供数据和模型工作流，但还要组合
+ Rasterio、GeoPandas、Shapely、PyTorch、OpenCV 或其他库，例如空间测量、
+ 成果质量检查和灾害统计。
+3. **产品业务能力**：不是 `geoai-py` 内置功能，而是利用其结果接入现有
+ 产品后实现，例如风险规则、事件工单、航线规划、集群调度、GeoLLM、知识
+ 图谱和模型治理。
+
+## 许可证边界
+
+`geoai-py` 项目本身采用 MIT 许可证。其依赖库、预训练模型、模型权重和数据集
+仍需逐项核查许可证；上层库的 MIT 许可证不会自动改变下游组件的授权要求。
+
+## 规划文件说明
+
+此前生成的规划文件保留作为历史版本。最新规划以文件名中带有
+`基于opengeos-geoai修订版` 的工作簿为准，避免把产品能力误称为
+`geoai-py` 内置能力。
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