Deep learning inference applied to multitemporal Sentinel-2 imagery. Our neural engine detects topological mutations and urban expansion patterns with high-fidelity classification accuracy.
Change Detection: Medellín Area
U-Net architecture optimized for high-resolution cadastral boundaries and spectral signature differentiation across multitemporal stacks.
Automated ingestion of ESA Copernicus data for continuous monitoring of informal urban settlements with 10m pixel resolution.
Raster-to-vector transformation using OpenCV and PostGIS to create validated cadastral mutation records under LADM-COL V3.