  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.
