AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING
Accurate and automatic building footprint extraction from single UAV images has become essential in many photogrammetry and remote sensing applications such as 3D building modeling, smart city, monitoring, disaster management, and urban planning. In this paper, the capability of U-Net architecture w...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/171/2023/isprs-annals-X-4-W1-2022-171-2023.pdf |
| _version_ | 1811177081371885568 |
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| author | Z. Farajzadeh M. Saadatseresht F. Alidoost |
| author_facet | Z. Farajzadeh M. Saadatseresht F. Alidoost |
| author_sort | Z. Farajzadeh |
| collection | DOAJ |
| description | Accurate and automatic building footprint extraction from single UAV images has become essential in many photogrammetry and remote sensing applications such as 3D building modeling, smart city, monitoring, disaster management, and urban planning. In this paper, the capability of U-Net architecture with ResNet as the backbone of the network is investigated to extract the building footprints from UAV-based orthophotos and normalized Digital Surface Models (nDSMs) considering the complementary nature of RGB and height information. The data has been captured from five non-overlapping rural scenes of Yazd province, Iran. After pre-processing, the training and test datasets are prepared to evaluate the performance of U-Net using different hyperparameters and input channels such as RGB (only orthophotos) and RGBD (orthophotos and nDSMs). The experiments highlight the effectiveness of height information to detect and extract the building footprints with significant improvements in precision from 89% to 97% and in recall from 77% to 91%. |
| first_indexed | 2024-04-10T22:56:03Z |
| format | Article |
| id | doaj.art-a2674f7381d54c34890ca65b4257ff10 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:56:03Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-a2674f7381d54c34890ca65b4257ff102023-01-14T11:00:41ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202217117710.5194/isprs-annals-X-4-W1-2022-171-2023AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNINGZ. Farajzadeh0M. Saadatseresht1F. Alidoost2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranPhotogrammetry and Geoinformatics, Faculty of Geomatics, Computer Science and Mathematics, Stuttgart University of Applied Sciences (HfT), GermanyAccurate and automatic building footprint extraction from single UAV images has become essential in many photogrammetry and remote sensing applications such as 3D building modeling, smart city, monitoring, disaster management, and urban planning. In this paper, the capability of U-Net architecture with ResNet as the backbone of the network is investigated to extract the building footprints from UAV-based orthophotos and normalized Digital Surface Models (nDSMs) considering the complementary nature of RGB and height information. The data has been captured from five non-overlapping rural scenes of Yazd province, Iran. After pre-processing, the training and test datasets are prepared to evaluate the performance of U-Net using different hyperparameters and input channels such as RGB (only orthophotos) and RGBD (orthophotos and nDSMs). The experiments highlight the effectiveness of height information to detect and extract the building footprints with significant improvements in precision from 89% to 97% and in recall from 77% to 91%.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/171/2023/isprs-annals-X-4-W1-2022-171-2023.pdf |
| spellingShingle | Z. Farajzadeh M. Saadatseresht F. Alidoost AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING |
| title_full | AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING |
| title_fullStr | AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING |
| title_full_unstemmed | AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING |
| title_short | AUTOMATIC BUILDING EXTRACTION FROM UAV-BASED IMAGES AND DSMs USING DEEP LEARNING |
| title_sort | automatic building extraction from uav based images and dsms using deep learning |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/171/2023/isprs-annals-X-4-W1-2022-171-2023.pdf |
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