Vector Maps Fusion for Reliable Mapping of Building Footprints

Recent technologies have enabled a significant growth of geographic datasets with different levels of detail and specifications. Subsequent analysis and mapping tasks may require to get the best out of the diversity of proposed sources. One solution is to integrate such maps through conflation. The purpose of this merging technique is to combine data that represent the same features from multiple datasets, into a new, richer dataset. Vector data conflation was intensively applied on linear networks like roads, streets and waterways, however combining polygonal building shapes has been relatively overlooked by the literature. In this paper, we propose an idea to aggregate a set of vector building maps to obtain a single fused representation. The proposed method takes as input two or more vector maps (more inputs lead to much more reliable maps), and decomposes the 2D space into a polygonal partition. A binary labelling procedure is applied using maps reliability weights, yielding contours of sets of connected buildings. Finally, a slicing algorithm decomposes contours into separate building instances. We show that our pipeline generates more accurate maps in terms of both IoU and F1 scores than any of the maps used as an input.