Deep learning mapping study demonstrates national extraction of over 5.4 million historical built-up polygons from 1950s topographic maps, highlighting long-term urbanization patterns.
This dataset provides a national vector data for built-up areas (BUA) and derived footprints envelopes extracted from the Institut National de l’Information Géographique et Forestière (IGN) SCAN 50 Historique—années 1950 raster maps. The source is a georeferenced mosaic of scanned topographic maps at 1:50,000, with a nominal ground resolution of 5 metres per pixel. The release candidate contains 5,458,764 machine-extracted BUA polygons and 128,504 derived footprint envelopes in RGF93 / Lambert-93 (EPSG:2154). Extraction combines a general binary semantic-segmentation model with a red-symbol- specific branch. Both use a ConvNeXt-Base backbone and UPerNet decoder. Model proba- bilities are thresholded and vectorised; predictions from overlapping tiles and from the two model branches are dissolved and combined. Settlement envelopes are subsequently derived from the mapped-building polygons using a 250 m contiguity rule, 100 m geometry simpli- fication, and a minimum of eight linked building polygons. The observations represent printed cartographic symbols. They are neither cadastral foot- prints nor a complete census of physically separate structures. A single polygon can represent an individual building, attached buildings, or a generalised block of built form.
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Litvine et al. (2026) studied this question.
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