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Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN

Stiller, Dorothee and Stark, Thomas and Wurm, Michael and Dech, Stefan and 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, pp. 1-4. IEEE. 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France. doi: 10.1109/jurse.2019.8808977. ISBN 978-172810009-8.

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/8808977

Abstract

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.

Item URL in elib:https://elib.dlr.de/128982/
Document Type:Conference or Workshop Item (Poster)
Title:Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Stiller, DorotheeUNSPECIFIEDhttps://orcid.org/0000-0002-8681-6144UNSPECIFIED
Stark, ThomasUNSPECIFIEDhttps://orcid.org/0000-0002-6166-7541UNSPECIFIED
Wurm, MichaelUNSPECIFIEDhttps://orcid.org/0000-0001-5967-1894UNSPECIFIED
Dech, StefanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Taubenböck, HannesUNSPECIFIEDhttps://orcid.org/0000-0003-4360-9126UNSPECIFIED
Date:May 2019
Journal or Publication Title:2019 Joint Urban Remote Sensing Event, JURSE 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/jurse.2019.8808977
Page Range:pp. 1-4
Publisher:IEEE
ISBN:978-172810009-8
Status:Published
Keywords:deep learning, aerial images, urban, building extraction, classification, very high-resolution
Event Title:2019 Joint Urban Remote Sensing Event (JURSE)
Event Location:Vannes, France
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, V - Transport und Klima (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Stiller, Dorothee
Deposited On:10 Sep 2019 09:11
Last Modified:11 Aug 2023 07:39

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