map repair deep cadastre maps alignment and temporal inconsistencies fix in satellite images

• DOI: 10.48550/arxiv.2007.12470
• Publication Date: 2020-09-26
• Authors: Zorzi, Stefano; Bittner, Ksenia; Fraundorfer, Frie...
• Keywords: FOS: Computer and information sciences; Photogrammetrie und Bildanalyse; Computer Vision and Pattern Recognition (cs.CV); segmentation; Computer Science - Computer Vision and Pattern Recognition; 0211 other engineering and technologies; deep learning; cadastre map alignment; 02 engineering and technology; high-resolution aerial images; remote sensing; building footprint; 11. Sustainability; 0202 electrical engineering, electronic engineering, information engineering

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction.