Polygonal Building Boundary Regularization in Suburban Areas Using Geometric Priors Based On Spatial Arrangement

Nowadays, the state-of-the-art in building extraction from remote sensing data is largely driven by deep learning methods. While it is feasible to supervise a model to output in an end-to-end fashion the vector footprints of building instances from high spatial resolution images, the availability of ground truth with high quality annotations may limit the success of this approach, which in fact is highly beneficial for downstream applications. An alternative strategy might be the post processing of building segmentation masks using geometric priors to obtain sharp and regularized polygonal footprints. Nonetheless, misalignment frequently occurs after the regularization, typically in suburban areas, since each building instance is processed independently of those in its neighborhood. To address misalignment, we propose in this study to take into account neighborhood through Voronoï partitions and identify groups of buildings that share similar geometric properties. The buildings constituting a local group are finally realigned and regularized as well using the same edge orientations extracted with the Line Segment Detector algorithm. Our proposal is assessed on a Pléiades image depicting a residential area of Montpellier in southern France.