Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1829-1832
Number of pages4
ISBN (Electronic)978-172816374-1
DOIs
Publication statusPublished - 26 Sep 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium: IGARS 2020 - Virtuell, United States
Duration: 26 Sep 20202 Oct 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium
CountryUnited States
CityVirtuell
Period26/09/202/10/20

Keywords

  • building footprint
  • cadastre map alignment
  • deep learning
  • high-resolution aerial images
  • remote sensing
  • segmentation

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Computer Science Applications

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