We analyze satellite and aerial imagery with deep learning to answer questions on the ground: what is growing where, how healthy is it, what has changed, and where is the risk. From satellite-imagery pipelines to interactive WebGIS dashboards.
Real Sentinel-2 imagery: change comparison, NDVI vegetation index and land-cover segmentation.
Imagery is real Sentinel-2; the masks and agent workflow are scripted and run in your browser. The production pipeline queries a STAC API and processes Cloud-Optimized GeoTIFFs.
Identify crop types and planted areas from multispectral time series - rice, fruit trees, industrial crops.
Track crop vigor across seasons with vegetation indices, spotting stress zones before yield loss.
Detect land-use change, construction, deforestation, and encroachment between image dates.
Map inundated areas from SAR/optical imagery and monitor drought indicators for early response.
Continuous deforestation and forest-degradation alerts for protected areas and plantations.
Deep-learning segmentation of remote sensing imagery, delivered as GIS layers and web maps.
Sentinel / Landsat
GDAL / Rasterio
U-Net / DeepLab
GeoTIFF / Vector
Leaflet / GeoServer
Combine satellite indices with Vectiq Field field data to estimate yields, delineate planting regions, and prioritize scouting - no drone required.
Automated change alerts for deforestation, riverbank erosion, and surface-water variation over large territories.
Land-cover classification and construction-change detection support planning compliance and infrastructure decisions.
