Multilabel Learning With ViT for Building Footprint Extraction From Off-Nadir Aerial Images

Abstract

The building footprint extraction (BFE) from aerial images is important for the creation and continuous monitoring of building inventories useful for urban planning, among others. Existing methods frequently extract roofs of buildings from aerial images assuming that they overlap with the footprint. This assumption does not hold in the case of off-nadir images. This letter proposes a novel multilabel learning of oblique building features - footprint, roof, and shape - with a Vision Transformer (ViT) for accurate BFE from off-nadir aerial images. A shape calculation algorithm is developed to derive shape polygons from the existing footprint and roof polygons. The method is compared with sever..