Across France we mapped land cover and land use into seven polygon classes — a high-resolution, vector inventory suited to environmental monitoring, agricultural analysis and planning at regional and national scale.
The challenge
France's range of landscapes — dense cities, agricultural plains, forests, rangelands and water — demands a consistent, automated and scalable classification. Manual digitisation and low-resolution satellite products could not deliver that reliably.
Our approach
We combined high-resolution satellite and aerial imagery with deep-learning semantic segmentation, classifying every pixel and vectorising the result into clean polygons with sub-metre accuracy where the imagery supports it. The seven classes were:
- forest, agriculture, rangeland, bareland
- urban, water, road
Results & benefits
- A nationwide / regional land-cover map with seven detailed classes.
- Roads delivered as polygons rather than lines — capturing width, so surface area can be measured directly.
- A repeatable workflow that supports annual updates to track deforestation, urban sprawl and agricultural change.
- Ready-to-use GIS outputs — Shapefile, GeoJSON and KML.
