Jens
faeb58ef6d
GeoIntel release gates / Compile, test, contracts and builds (push) Successful in 1m49s
GeoIntel release gates / Python and npm vulnerability policy (push) Successful in 21s
GeoIntel release gates / Production AI image, SBOM and container scan (push) Successful in 5m39s
GeoIntel release gates / Deploy exact gated revision to Unraid (push) Failing after 58m43s
127 lines
2.2 KiB
Markdown
127 lines
2.2 KiB
Markdown
# GeoIntel Kempen — Segmentation Pipeline Specification v1.0
|
|
|
|
The segmentation pipeline converts raster imagery into georeferenced masks and polygons.
|
|
|
|
## Goal
|
|
|
|
Produce class masks for buildings, vegetation, water, or other targets and convert those masks to GIS layers.
|
|
|
|
## Model families
|
|
|
|
V1/early:
|
|
|
|
- YOLO segmentation model if available.
|
|
- Segment Anything for prompt-based or automatic masks.
|
|
|
|
Later:
|
|
|
|
- U-Net
|
|
- DeepLab
|
|
- custom PyTorch semantic segmentation
|
|
|
|
## Pipeline
|
|
|
|
```text
|
|
Raster dataset
|
|
↓
|
|
Raster validation
|
|
↓
|
|
Tile generation
|
|
↓
|
|
Segmentation inference
|
|
↓
|
|
Mask stitching
|
|
↓
|
|
Georeferenced mask output
|
|
↓
|
|
Polygonization
|
|
↓
|
|
Geometry cleanup
|
|
↓
|
|
PostGIS storage
|
|
↓
|
|
Map overlay + statistics
|
|
```
|
|
|
|
## Mask types
|
|
|
|
### Instance segmentation
|
|
|
|
Each object has its own mask.
|
|
|
|
### Semantic segmentation
|
|
|
|
Each pixel has a class value.
|
|
|
|
V1 may support instance segmentation first.
|
|
|
|
## Output storage
|
|
|
|
- Raw mask files: `storage/masks/{project_id}/{analysis_run_id}/`
|
|
- Polygonized outputs in PostGIS `segmentations` table.
|
|
- Derived vector layer for map rendering.
|
|
|
|
## Polygonization rules
|
|
|
|
1. Convert binary/class mask to shapes using raster transform.
|
|
2. Discard polygons below minimum area threshold.
|
|
3. Repair geometries.
|
|
4. Simplify for display only, keep analysis geometry if possible.
|
|
5. Calculate area.
|
|
|
|
Default thresholds:
|
|
|
|
```yaml
|
|
minimum_area_m2: 1.0
|
|
simplify_tolerance_m: 0.10
|
|
```
|
|
|
|
## Required segmentation record
|
|
|
|
```yaml
|
|
analysis_run_id
|
|
class_name
|
|
geometry
|
|
area_m2
|
|
confidence
|
|
raster_mask_path
|
|
source_tile_id
|
|
model_name
|
|
metadata_json
|
|
```
|
|
|
|
## Metrics
|
|
|
|
- total segmented area by class
|
|
- polygon count by class
|
|
- mean confidence by class
|
|
- largest polygon by class
|
|
- area ratio against selected area
|
|
|
|
## API
|
|
|
|
```http
|
|
POST /analysis/segmentation
|
|
GET /analysis/{id}/segmentations
|
|
POST /analysis/{id}/polygonize
|
|
POST /analysis/{id}/exports/masks
|
|
POST /analysis/{id}/exports/geojson
|
|
```
|
|
|
|
## UI
|
|
|
|
Segmentation Lab must show:
|
|
|
|
- raster selector
|
|
- model selector
|
|
- segmentation mode
|
|
- class list
|
|
- threshold controls
|
|
- mask preview
|
|
- polygon overlay
|
|
- export buttons
|
|
|
|
## V1 target
|
|
|
|
Implement the data structures, job pipeline contracts, mask storage, polygonization utilities and UI shell. Full SAM integration can follow after raster/detection foundation is stable.
|