169 lines
3.0 KiB
Markdown
169 lines
3.0 KiB
Markdown
# GeoIntel Kempen — Detection Pipeline Specification v1.0
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The detection pipeline converts geospatial imagery into georeferenced object detections stored as GIS features.
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## Goal
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Run object detection on aerial or satellite imagery and export results as map layers and GeoJSON.
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## Supported model family
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V1 target:
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- Ultralytics YOLO detection model.
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Later:
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- custom PyTorch models
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- OpenMMLab detectors
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## Pipeline
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```text
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Raster dataset
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↓
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Raster validation
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↓
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Tile generation
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↓
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Model inference per tile
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↓
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Detection postprocessing
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↓
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Pixel coordinates → geospatial coordinates
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↓
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Merge / de-duplicate overlapping detections
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↓
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Store detections in PostGIS
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↓
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Render as map overlay
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↓
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Export as GeoJSON/CSV
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```
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## Step 1 — Validation
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Check:
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- raster exists
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- raster is readable
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- raster has CRS and transform
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- selected bands are valid
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- tile size is valid
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- model file exists or selected model is available
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## Step 2 — Tile generation
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Parameters:
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```yaml
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tile_size_px: 640
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overlap_px: 96
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bands: [1, 2, 3]
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area_id: optional
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```
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Every tile must store:
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- tile path
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- source pixel window
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- affine transform
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- bounds
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- source dataset id
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## Step 3 — Inference
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For each tile:
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- convert to model input image
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- run YOLO
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- collect boxes, classes, confidence
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Raw detection format:
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```json
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{
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"tile_id": "uuid",
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"class_name": "building",
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"confidence": 0.91,
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"bbox_px": [x1, y1, x2, y2]
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}
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```
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## Step 4 — Georeferencing
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Convert pixel bbox corners using the tile affine transform.
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Output geometry:
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- polygon footprint of bbox for detection models
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- centroid point optional
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- original pixel bbox kept in metadata
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## Step 5 — Merge and de-duplicate
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Because tiles overlap, duplicated detections must be removed.
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Default method:
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- group by class
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- calculate IoU between overlapping geospatial bboxes
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- apply non-maximum suppression by confidence
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- default IoU merge threshold: 0.5
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- implemented for configured-YOLO as EPSG:4326 cross-tile duplicate suppression
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before `Detection` persistence; `YOLO_DUPLICATE_IOU_THRESHOLD=0` disables the
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GeoIntel-side pass for debugging.
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## Step 6 — Storage
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Store each detection:
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```yaml
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analysis_run_id
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class_name
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confidence
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geometry
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bbox_json
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source_tile_id
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model_name
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model_version
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metadata_json
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```
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## Step 7 — Metrics
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Calculate:
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- detection count by class
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- mean confidence
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- low confidence count
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- confidence histogram
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- detected area by class when polygon geometry is valid
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## API
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```http
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POST /analysis/object-detection
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GET /analysis/{id}
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GET /analysis/{id}/detections
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POST /analysis/{id}/exports/geojson
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```
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## UI
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Detection Lab must allow:
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- raster selection
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- model selection
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- class selection
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- confidence threshold
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- tile size
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- overlap
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- run detection
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- map overlay visibility
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- QA/QC handoff button
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## V1 acceptable model strategy
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If no locally trained model exists yet, the system may support a configurable YOLO model path and clearly mark model classes as model-dependent. The code must not fake detections.
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