End-to-end roofline extraction from very-high-resolution remote sensing images

Roof shape information is essential for creating 3D building models. However, the automated extracting of roof structures from Earth observation data is a difficult task involving significant uncertainties caused by scene complexity and limited multi-source data coverage. This paper introduces the integrally-attracted wireframe parsing (IAWP) framework to reconstruct building rooflines as a planar graph from remotely sensed images with a single forward pass. We add global geometric line priors through the Hough transform into deep networks to better extract the linear geometric features. We perform experiments on the vectorizing world building (VWB) dataset. The investigated method improves the F-score metrics of corner points/edges by 0.1%/7.7% and 0.6%/1.1%, respectively. Visual comparison results also indicate that the HT-IHT block gives consistent improvements in terms of geometric regularity.