TY - GEN
T1 - Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
AU - Zorzi, Stefano
AU - Bittner, Ksenia
AU - Fraundorfer, Friedrich
PY - 2020/9/26
Y1 - 2020/9/26
N2 - 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.
AB - 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.
KW - building footprint
KW - cadastre map alignment
KW - deep learning
KW - high-resolution aerial images
KW - remote sensing
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85102017641&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9323370
DO - 10.1109/IGARSS39084.2020.9323370
M3 - Conference paper
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1829
EP - 1832
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Y2 - 26 September 2020 through 2 October 2020
ER -