# Demo Use Cases

These are the workflows GeoIntel must be built around. Codex should prioritize these over generic feature expansion.

## Demo 1 — Building Detection + GRB QA

### Purpose
Show the exact portfolio fit for GeoAI engineering: raster input, computer vision, georeferenced detections, reference-data comparison, QA metrics and GIS export.

### Area
Default: Geel or another Kempen area with mixed buildings and roads.

### Input
- Georeferenced aerial image or GeoTIFF.
- Reference building layer from GRB or local fixture.

### Pipeline
1. Create project.
2. Define area.
3. Import raster.
4. Fetch/cache GRB building reference layer.
5. Tile raster.
6. Run YOLO/object-detection provider.
7. Convert detections back to geospatial polygons or boxes.
8. Store detections in PostGIS.
9. Run QA/QC against GRB building footprints.
10. Show results on map.
11. Export detections and QA report.

### Required outputs
- Detection layer.
- GRB reference layer.
- Matched features layer.
- False positives layer.
- False negatives layer.
- Precision/recall/F1/IoU dashboard.
- GeoJSON export.

### Portfolio message
“I built a GeoAI pipeline that detects buildings on imagery and validates the results against official Flemish reference geometry.”

## Demo 2 — Vegetation Segmentation

### Purpose
Show segmentation and raster/vector conversion.

### Input
- Raster image or Sentinel-derived raster later.
- Optional area polygon.

### Pipeline
1. Import raster.
2. Run segmentation provider or thresholded index workflow.
3. Produce mask.
4. Polygonize mask.
5. Calculate area and coverage percentage.
6. Visualize mask and polygons.
7. Export GeoJSON/mask.

### Required outputs
- Segmentation mask.
- Polygonized vegetation layer.
- Area statistics.
- Export package.

## Demo 3 — Vector Change Detection

### Purpose
Show geospatial comparison before full remote-sensing change detection is implemented.

### Input
- Two reference/detection vector layers for different dates or runs.

### Pipeline
1. Select baseline layer.
2. Select comparison layer.
3. Normalize CRS.
4. Match overlapping objects.
5. Identify added/removed/changed geometries.
6. Calculate area deltas.
7. Show change layer.

### Required outputs
- Added features.
- Removed features.
- Changed features.
- Change statistics.
- GeoJSON export.

## Demo 4 — Raster Indices Preview

### Purpose
Prepare the Sentinel/remote-sensing part.

### Input
- Multiband raster with suitable bands.

### Pipeline
1. Select bands.
2. Calculate NDVI/NDWI/NDBI where bands exist.
3. Render preview.
4. Calculate min/max/mean/histogram.
5. Extract thresholded areas later.

### Required outputs
- Index raster artifact.
- Histogram/statistics.
- Preview overlay.

## Demo priority
Build in this order:
1. Demo 1 first.
2. Demo 3 second.
3. Demo 2 third.
4. Demo 4 fourth.

Demo 1 is the main recruiter-facing workflow.