AI RESEARCH
GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery
arXiv CS.CV
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ArXi:2602.09932v2 Announce Type: replace Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and population mapping, yet globally consistent data remain scarce. In this work, we develop GeoFormer, a lightweight Swin Transformer-based multi-task learning framework that jointly estimates BH and BF on a 100m grid using only open-access Sentinel-1 SAR, Sentinel-2 multispectral, and DEM data. A geo-blocked data-splitting strategy enforces strict spatial independence between.