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| | | `pip check` passes in this environment. |
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| | | Dedicated anomaly-detection environment: |
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| | | `D:\lgh\code\geoai-workbench\.venvs\09-anomaly-detection` |
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| | | | Component | Version | |
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| | | | --- | --- | |
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| | | | Python | 3.12.10 | |
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| | | | geoai-py | 0.42.0 | |
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| | | | scikit-learn | 1.9.0 | |
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| | | | SciPy | 1.18.0 | |
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| | | | rasterio | 1.5.1 | |
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| | | | geopandas | 1.1.4 | |
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| | | | OpenCV | 4.14.0 | |
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| | | The environment is CPU-only, contains no model weights, and `pip check` passes. |
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| | | Dedicated semantic-mapping environment: |
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| | | `E:\AllWorkProject\geoai-workbench\.venvs\02-semantic-mapping` |
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| | |
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| | | - Start command: `py -3.12 .\scripts\serve_workbench_console.py` |
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| | | - 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. |
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| | | - Scope: independent local experiment workbench for this repository. It has no code, account, or product API link to the two drone-product repositories. The map client directly requests public ArcGIS tiles and, only when configured, TianDiTu tiles. |
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| | | - Current workflow: `00-change-detection`, `01-object-detection`, `02-semantic-mapping`, `04-spatial-measurement`, and `15-trajectory-analysis` support new local runs, searchable case libraries, and visual result workspaces. Change detection shows two-date imagery, a ChangeStar raster overlay, pixel-coordinate GeoJSON, per-feature probability stats and downloads. Its scan workspace shows historical scans and accepts new user-uploaded pairs with selectable threshold/minimum-area combinations, asynchronous CPU execution, per-combination overlays/statistics and GeoJSON, without treating scan inference as a formal model run. Spatial measurement shows raster/vector results, per-object metrics and downloads. Change detection now uploads large TIFFs as raw binary streams and copies bytes into a new run directory before invoking the fixed capability script; the original file is not rewritten. The server creates a new run ID for every submission, only accepts allowlisted input types and enabled tasks, and calls fixed capability scripts in fixed virtual environments. |
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| | | - Current workflow: `00-change-detection`, `01-object-detection`, `02-semantic-mapping`, `04-spatial-measurement`, `09-anomaly-detection`, and `15-trajectory-analysis` support new local runs, searchable case libraries, and visual result workspaces. Anomaly detection accepts separate normal-reference and inspection groups through bounded binary uploads, runs asynchronously, and shows references, input/output comparison, heatmaps, candidates and downloads. Change detection shows two-date imagery, a ChangeStar raster overlay, pixel-coordinate GeoJSON, per-feature probability stats and downloads. Its scan workspace accepts new user-uploaded pairs with selectable threshold/minimum-area combinations, asynchronous CPU execution, per-combination overlays/statistics and GeoJSON, without treating scan inference as a formal model run. Spatial measurement shows raster/vector results, per-object metrics and downloads. Change detection and anomaly detection upload large raster/image files as raw binary streams and copy bytes into new run directories before fixed capability scripts run; source files are not rewritten. The server creates a new run ID for every submission, only accepts allowlisted input types and enabled tasks, and calls fixed virtual-environment scripts. |
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| | | - File exposure: the static handler permits only console assets, `shared/outputs`, and source images required for result comparison. Upload APIs write new raw and processed run directories but do not expose the rest of the repository. |
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| | | - Styling: `apps/workbench-console/src/styles.css` is organized into design variables, application shell, shared workbench components, capability workspaces, maps, and responsive rules. New capability pages must use a scoped workspace class and shared spacing variables instead of global override patches. |
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| | | | `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. | |
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| | | | `02-semantic-mapping` | Runnable CPU baseline verified | B capability: deterministic RGB/HSV segmentation plus `geoai.masks_to_vector`; raster and vector outputs are inspectable, but this is not a trained GeoAI model. | |
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| | | | `04-spatial-measurement` | Runnable CPU Demo verified | B capability: `geoai.masks_to_vector` plus ecosystem measurement; counts labelled raster objects and reports area/perimeter in projected or explicitly non-metric pixel/coordinate units. | |
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| | | | `09-anomaly-detection` | Runnable CPU CLI Demo verified | B capability: interpretable image features plus robust rules and Isolation Forest score visual outliers; `geoai-py` supplies `masks_to_vector`. Results are review candidates, not business-event conclusions. | |
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| | | | `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. | |
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| | | | `03`, `05` through `14`, `16` through `18` | Directory and initial README only | No verified local Demo yet. Start each one through `$geoai-capability-builder`. | |
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| | | | `03`, `05` through `08`, `10` through `14`, `16` through `18` | Directory and initial README only | No verified local Demo yet. Start each one through `$geoai-capability-builder`. | |
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| | | ## Semantic Mapping Snapshot |
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| | | ## Anomaly Detection Snapshot |
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| | | - Script: `capabilities/09-anomaly-detection/run_anomaly_detection.py`; reproducible input generator: `generate_validation_inputs.py` in the same directory. |
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| | | - Boundary: B. Seventeen interpretable RGB/HSV/texture features feed a robust MAD rule baseline and a 200-tree Isolation Forest; fixed-view inputs additionally use class-agnostic corresponding-location Lab appearance and grayscale structure differences after image-wide normalization. `geoai-py 0.42.0` only supplies `masks_to_vector`. There are no model weights or colour/object-specific rules. |
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| | | - Contract: image or directory input; minimum 50 reference windows; outputs are two heatmaps, comparison overlay, encoded mask GeoTIFF, candidate GeoJSON, tile CSV and run metadata. Ordinary images use pixel coordinates; valid GeoTIFF CRS/transform is preserved. |
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| | | - Validation: controlled color, blur and dark anomalies were injected into a copy of `07-16.jpg`; `07-19.jpg` was used unchanged as a difficult real sample. Source-copy SHA256 values match the originals. |
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| | | - Measured result: 588 reference windows and three inputs completed in 41.051 seconds CPU. The rule method hit 3/3 controlled regions with precision 0.583, recall 0.672 and IoU 0.454; Isolation Forest hit 2/3 with precision 0.831, recall 0.500 and IoU 0.454. Normal-image coverage was 0.33% and 0.00%, respectively. Every image completed within 15 seconds. |
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| | | - Visual limitation: the normal image has a small rule false positive at a vegetation/rock transition. Difficult-image candidates occur around concrete structure, a dark opening and a top dark region, consistent with viewpoint/material/lighting shift; no real anomaly or business accuracy is claimed. |
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| | | - Console: `GET/POST /api/anomaly-detection/runs`, binary `PUT /api/anomaly-detection/uploads/<upload-id>` and asynchronous `GET /api/anomaly-detection/jobs/<job-id>`. A real one-reference/one-input upload produced run `anomaly-20260818-102845-61f55b` with six review candidates. Desktop and 390 px layouts were visually checked; tables use internal horizontal scrolling and the collapsed menu trigger remains inside the viewport. |
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| | | - Synthetic fixed-view validation: five ImageGen normal references cover overcast, sunny, after-rain, morning-haze and late-afternoon conditions; one test image adds a localized branch/leaf/mud/rock blockage. The original pooled-feature run returned zero candidates and is retained as `ImageGen 五参考图堵塞复核（漏检案例）`. |
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| | | - Fixed-view optimization: `--spatial-mode auto` now selects same-position robust rules when 3+ references and all target images share dimensions, after normalizing each image's global feature profile; otherwise it falls back to the original global rule. At the unchanged 0.995 quantile, the synthetic blockage produced one correctly located rule candidate with 9.375% coverage. Five leave-one-normal-out checks produced 7.292%, 0%, 0%, 0%, and 0% coverage. The successful output is retained as `ImageGen 五参考图堵塞复核（同位置优化）`; Isolation Forest still missed this sample. |
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| | | - Generic local-change enhancement: same-position mode now also calibrates nearest-normal-reference Lab appearance and grayscale intensity/edge/contrast distances. A previously missed small added pattern became one 9,467-pixel rule candidate with 100% coverage of its 6,710 validation pixels and 0.602% full-image coverage; it is retained as `通用局部异常增强（小目标漏检修复）`. The blockage regression remains one candidate at 9.594% rule coverage. Five leave-one-normal-out combined coverages are 7.292%, 0%, 0%, 0.460%, and 1.209%. No sample colour, object class, or fixed coordinate is encoded. |
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| | | - Next decision: add image registration and/or a channel ROI or specialist blockage detector before accepting camera motion or claiming blockage semantics. Obtain real normal/labelled anomaly pairs; the synthetic result is only a pipeline check. |
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| | | ## Change Detection Snapshot |
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| | | - Measured result: real pair at 1024 px / tile 512 / overlap 64 took 41.556 seconds on CPU, found 755 inliers from 769 ORB matches, 98.52% valid area, 674 changed pixels (0.0857%) and one polygon. Self-comparison at 512 px took 25.902 seconds and found zero changed pixels and zero polygons. On the urban parking sample, increasing from 1024 to 2048 produced 16 polygons instead of one and covered more vehicle/parking changes, but took about 84 seconds CPU and still had misses/false positives. |
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| | | - GeoTIFF grid-alignment validation: uploaded `1.tif` (6217 x 5525) and `2.tif` (6218 x 5497) share `EPSG:32650` but have slightly different origins and pixel sizes. The second image was reprojected bilinearly to the first grid in the processing copy, then completed at 1024 px in 80.882 seconds with georeferenced outputs. SHA256 checks confirmed the source staging files and their raw run copies are byte-identical. |
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| | | - Honest limitation: ChangeStar weights are trained for Changen2/S1 building change and the close-up rock/concrete scene is out of distribution. No manual truth exists, so no precision, recall, IoU, change type, or engineering alert is claimed. Pixel GeoJSON has no CRS; the downloaded model is CC BY-NC-SA 4.0 and not cleared for commercial use. |
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| | | - Console: `GET/POST /api/change-detection/runs`, `GET/POST /api/change-detection/scans`, `GET /api/change-detection/scan-jobs/<job-id>`, `POST /api/change-detection/scans/<scan-id>/promote` and binary upload endpoints on local `6xxx` ports. Real uploads at `6176` verified thresholds and selectable processing resolution; scan submissions are validated for at most 24 combinations and execute asynchronously through fixed capability scripts. Scan inference outputs remain in the scan workspace until a user promotes one selected combination; promotion creates a new formal run directory with raw-input references, makes the case discoverable, and uses rule rectangles as the primary case vector while retaining original polygons and masks. GeoTIFF candidates include source-CRS and EPSG:4326 vectors; ordinary images remain in px coordinates. Minimum connected area is a processed-pixel filter for both modes; an empty console scan selection defaults to `256 px`. Binary upload filenames are percent-encoded in the browser and decoded/sanitized by the server, so Chinese filenames no longer fail Request header validation. The historical candidate `0.50/256` was promoted and appeared in the case library with 42 WGS84 rectangles. Auto/image and GeoTIFF mode handling is covered by unit tests. Separate raw before/after directories and a separate processed directory are used; unsafe basename sanitization plus invalid/oversized request rejection were verified. |
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| | | - Console: `GET/POST /api/change-detection/runs`, `GET/POST /api/change-detection/scans`, `GET /api/change-detection/scan-jobs/<job-id>`, `POST /api/change-detection/scans/<scan-id>/promote` and binary upload endpoints on local `6xxx` ports. Real uploads at `6176` verified thresholds and selectable processing resolution; scan submissions are validated for at most 24 combinations and execute asynchronously through fixed capability scripts. Scan inference outputs remain in the scan workspace until a user promotes one selected combination; promotion creates a new formal run directory with raw-input references, makes the case discoverable, and uses rule rectangles as the primary case vector while retaining original polygons and masks. GeoTIFF candidates include source-CRS and EPSG:4326 vectors; ordinary images remain in px coordinates. Minimum connected area is a processed-pixel filter for both modes; an empty console scan selection defaults to `256 px`. Vector export now uses `rasterio.features.shapes` after the pixel-area filter, rather than GeoAI's morphological/NMS vector helper: a GeoTIFF validation changed from 42 exported vectors to all 308 retained mask regions, with 308 rectangles and 308 WGS84 features; every original polygon was covered by its matching rectangle. A non-overwriting corrected scan `scan-20260819-063712-744f45-vector-fixed` is available for review. Binary upload filenames are percent-encoded in the browser and decoded/sanitized by the server, so Chinese filenames no longer fail Request header validation. Auto/image and GeoTIFF mode handling is covered by unit tests. Separate raw before/after directories and a separate processed directory are used; unsafe basename sanitization plus invalid/oversized request rejection were verified. |
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| | | - Next decision: obtain georeferenced same-GSD orthophotos and manually labelled change masks for the intended business class, then compare registration error, false positives, misses and IoU before any batch or product use. |
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| | | - Script: `capabilities/02-semantic-mapping/run_semantic_segmentation.py`. |
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| | | - `scripts/serve_workbench_console.py` 仅监听 `127.0.0.1` 等回环地址，支持轨迹与目标检测的受限 JSON/Base64 上传 API；每次生成唯一运行目录，不覆盖原始数据或既有输出。 |
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| | | - 轨迹上传入口接受 XLSX、KMZ、禁飞区 GeoJSON 和可选适飞区 Gzip，预处理器已支持缺少适飞区时继续生成可运行 case。 |
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| | | - 目标检测页面默认并列展示原图与标注图；案例数据从输出目录扫描，历史多模型检测结果可以在同一案例库中选择。 |
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| | | - 异常检测案例库支持把历史案例的原始参考图、待检测图和运行参数回填到新运行表单，供用户调整后创建新的检测运行；回填操作不自动运行，也不覆盖历史案例。 |
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| | | - 轨迹汇总的 `normal`、`stop`、`route_deviation`、`restricted_zone`、`gathering` 标签在 UI 显示中文，原始英文值保留在 CSV/GeoJSON。 |
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