hbku/satellite-landuse-classifier
Satellite Land-Use Classifier
mIoU
0.81
Pixel accuracy
0.92
F1 (urban)
0.90
F1 (mangrove)
0.78
About this model
Semantic segmentation model that classifies Sentinel-2 imagery of Qatar into nine land-use classes including urban fabric, industrial, sabkha, mangrove, and reclaimed land. Produces 10 m resolution masks with per-pixel confidence suitable for change detection.
Intended use
Environmental monitoring, urban-expansion tracking, and coastal-zone reporting by planning and environment entities and researchers.
Training data lineage
Trained on qatar-sentinel2-tiles with 11,400 hand-annotated tile masks spanning 2021-2025 seasonal composites.
Limitations & bias notes
Mangrove and intertidal classes are confused at extreme low tide, and freshly graded construction sites are sometimes labelled as sabkha. Cloud and dust masking relies on upstream scene flags and can leak haze artefacts into predictions.
Evaluation metrics
| mIoU | 0.81 |
| Pixel accuracy | 0.92 |
| F1 (urban) | 0.90 |
| F1 (mangrove) | 0.78 |
Try it
Live sandboxsentinel2_alrayyan_2026Q2.tif
fixtureFeedback
Owning entity
- Updated
- 2026-04-19
- Latest version
- 1.2.0
- License
- Open
- Access
- Open
Trained on
qatar-sentinel2-tiles →