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Efficiency of CNNs for Building Extraction: Comparative Analysis of Performance and Time

Stiller, Dorothee and Stark, Thomas and Strobl, Verena and Leupold, Maike and Wurm, Michael and Taubenböck, Hannes (2023) Efficiency of CNNs for Building Extraction: Comparative Analysis of Performance and Time. In: 2023 Joint Urban Remote Sensing Event (JURSE), pp. 1-4. IEEE. 2023 Joint Urban Remote Sensing Event (JURSE), 17.-19. Mai 2023, Heraklion, Greece. doi: 10.1109/JURSE57346.2023.10144140.

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Official URL: https://ieeexplore.ieee.org/document/10144140


Openly available geodata of buildings are still incomplete or missing for many regions of the world. Convolutional neural networks (CNNs) have shown to be suitable for building extraction and thus, can help to overcome these shortcomings. In this study, we compare 16 encoder-decoder combinations for the task of building extraction from very high-resolution (VHR) aerial imagery in terms of performance, time needed for training and validation, and, efficiency. Therefore, we train and evaluate nine encoder models using a Feature Pyramid Network (FPN) decoder, and seven decoder models using a residual neural network (ResNet) encoder, more specifically ResNet50. The analysis is performed for two types of input data: RGB-NIR and RGB-NIR-nDSM. The results reveal that the majority of the investigated segmentation models show a high similarity in the area of performance, whereas the time needed for training and validation is exceptionally different. Both parameters, performance and time, are combined for an efficiency ranking, and the models are ranked accordingly. We found that a ResNet50 and FPN combination is the most suitable for our application. The presented results help to evaluate how each model combination should be rated in terms of efficiency for building extraction.

Item URL in elib:https://elib.dlr.de/196035/
Document Type:Conference or Workshop Item (Poster)
Title:Efficiency of CNNs for Building Extraction: Comparative Analysis of Performance and Time
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Stiller, DorotheeUNSPECIFIEDhttps://orcid.org/0000-0002-8681-6144UNSPECIFIED
Wurm, MichaelUNSPECIFIEDhttps://orcid.org/0000-0001-5967-1894UNSPECIFIED
Taubenböck, HannesUNSPECIFIEDhttps://orcid.org/0000-0003-4360-9126UNSPECIFIED
Journal or Publication Title:2023 Joint Urban Remote Sensing Event (JURSE)
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Keywords:building extraction, convolutional neural network, encoder, decoder, comparative analysis, efficiency, aerial imagery, semantic segmentation
Event Title:2023 Joint Urban Remote Sensing Event (JURSE)
Event Location:Heraklion, Greece
Event Type:international Conference
Event Dates:17.-19. Mai 2023
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 - DATAMOST - Daten & Modelle zur Mobilitätstransform
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Stiller, Dorothee
Deposited On:18 Sep 2023 08:50
Last Modified:18 Sep 2023 08:50

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