Zorzi, Stefano und Bittner, Ksenia und Fraundorfer, Friedrich (2020) Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images. In: 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Seiten 1-4. IGARSS 2020, 2020-09-26 - 2020-10-02, Virtual Symposium. doi: 10.1109/igarss39084.2020.9323370. ISBN 978-172816374-1. ISSN 2153-6996.
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Kurzfassung
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.
elib-URL des Eintrags: | https://elib.dlr.de/138187/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||
Titel: | Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images | ||||||||||||||||
Autoren: |
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Datum: | September 2020 | ||||||||||||||||
Erschienen in: | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
DOI: | 10.1109/igarss39084.2020.9323370 | ||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||
Name der Reihe: | IEEE International Geoscience and Remote Sensing Symposium | ||||||||||||||||
ISSN: | 2153-6996 | ||||||||||||||||
ISBN: | 978-172816374-1 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | deep learning, segmentation, building footprint, remote sensing, high-resolution aerial images, cadastre map alignment | ||||||||||||||||
Veranstaltungstitel: | IGARSS 2020 | ||||||||||||||||
Veranstaltungsort: | Virtual Symposium | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 26 September 2020 | ||||||||||||||||
Veranstaltungsende: | 2 Oktober 2020 | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Verkehr | ||||||||||||||||
HGF - Programmthema: | Straßenverkehr | ||||||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||||||
DLR - Forschungsgebiet: | V ST Straßenverkehr | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | V - NGC KoFiF (alt) | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse | ||||||||||||||||
Hinterlegt von: | Bittner, Ksenia | ||||||||||||||||
Hinterlegt am: | 26 Nov 2020 14:01 | ||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:40 |
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