Stiller, Dorothee und Stark, Thomas und Wurm, Michael und Dech, Stefan und Taubenböck, Hannes (2019) Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN. In: 2019 Joint Urban Remote Sensing Event, JURSE 2019, Seiten 1-4. IEEE. 2019 Joint Urban Remote Sensing Event (JURSE), 2019-05-22 - 2019-05-24, Vannes, France. doi: 10.1109/jurse.2019.8808977. ISBN 978-172810009-8.
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Offizielle URL: https://ieeexplore.ieee.org/document/8808977
Kurzfassung
Urban areas are hotspots of complex and dynamic alterations of the Earth’s surface. Using deep learning (DL) techniques in remote sensing applications can significantly contribute to document these tremendous changes. Open source building data at a very high level of detail are still scarce or incomplete for many regions, therefore, hindering research and policy to properly provide knowledge on urban structures. In this study, we use a convolutional neural network to extract buildings for the city of Santiago de Chile. We deploy the recently released Mask R-CNN and use a pretrained model (PM) which already has been trained with remote sensing imagery. We fine-tune PM with very high-resolution (VHR) airborne RGB images from our study region and generate the fine-tuned model (FM). To extend the number of training data, we test several data augmentation methods for training purposes and evaluate their performance in context of urban environments. We achieve highest overall accuracy of 92 % by using augmentations and the generated FM. Our findings encourage to use DL methods in the urban context. The presented method can be adapted and applied to other global urban regions, and, help to overcome lacks in open source building data to assess urban environments.
elib-URL des Eintrags: | https://elib.dlr.de/128982/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||||||
Titel: | Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN | ||||||||||||||||||||||||
Autoren: |
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Datum: | Mai 2019 | ||||||||||||||||||||||||
Erschienen in: | 2019 Joint Urban Remote Sensing Event, JURSE 2019 | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
DOI: | 10.1109/jurse.2019.8808977 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||||||
ISBN: | 978-172810009-8 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | deep learning, aerial images, urban, building extraction, classification, very high-resolution | ||||||||||||||||||||||||
Veranstaltungstitel: | 2019 Joint Urban Remote Sensing Event (JURSE) | ||||||||||||||||||||||||
Veranstaltungsort: | Vannes, France | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 22 Mai 2019 | ||||||||||||||||||||||||
Veranstaltungsende: | 24 Mai 2019 | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Fernerkundung u. Geoforschung, V - Transport und Klima (alt) | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit Deutsches Fernerkundungsdatenzentrum > Leitungsbereich DFD | ||||||||||||||||||||||||
Hinterlegt von: | Stiller, Dorothee | ||||||||||||||||||||||||
Hinterlegt am: | 10 Sep 2019 09:11 | ||||||||||||||||||||||||
Letzte Änderung: | 07 Jun 2024 11:12 |
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