COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA
In this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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-W1-2022/165/2023/isprs-annals-X-4-W1-2022-165-2023.pdf |
| _version_ | 1811177056853032960 |
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| author | B. Farajelahi M. Najaf H. Arefi |
| author_facet | B. Farajelahi M. Najaf H. Arefi |
| author_sort | B. Farajelahi |
| collection | DOAJ |
| description | In this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster management, and city planning. Building detection techniques can basically be divided into two categories: the classical approach and the deep learning approach. The main goal of this study is to compare some commonly used detection techniques in photogrammetry, like segmentation-based and classification-based methods using digital elevation data as input. The 4 different methods of roof detection with their detailed analysis and their final results are presented in this paper. This study encourages researchers to further advance research in building detection techniques. Results show that the 2D region growing can successfully segment the building components like the main facades of the complex roof and provide accurate qualitative and quantitative results compared to the other methodologies used in this study. |
| first_indexed | 2024-04-10T22:55:38Z |
| format | Article |
| id | doaj.art-3d8b9f3476424dfbbcdfa7c63037aa56 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-10T22:55:38Z |
| 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-3d8b9f3476424dfbbcdfa7c63037aa562023-01-14T11:00:09ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-01-01X-4-W1-202216517010.5194/isprs-annals-X-4-W1-2022-165-2023COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATAB. Farajelahi0M. Najaf1H. Arefi2Department of Geomatics, University College of Engineering, University of Tehran, Tehran, IranDepartment of Geomatics, University College of Engineering, University of Tehran, Tehran, IranDepartment of Geomatics, University College of Engineering, University of Tehran, Tehran, IranIn this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster management, and city planning. Building detection techniques can basically be divided into two categories: the classical approach and the deep learning approach. The main goal of this study is to compare some commonly used detection techniques in photogrammetry, like segmentation-based and classification-based methods using digital elevation data as input. The 4 different methods of roof detection with their detailed analysis and their final results are presented in this paper. This study encourages researchers to further advance research in building detection techniques. Results show that the 2D region growing can successfully segment the building components like the main facades of the complex roof and provide accurate qualitative and quantitative results compared to the other methodologies used in this study.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/165/2023/isprs-annals-X-4-W1-2022-165-2023.pdf |
| spellingShingle | B. Farajelahi M. Najaf H. Arefi COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA |
| title_full | COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA |
| title_fullStr | COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA |
| title_full_unstemmed | COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA |
| title_short | COMPARING THE PERFORMANCE OF ROOF SEGMENTATION METHODS IN AN URBAN ENVIRONMENT USING DIGITAL ELEVATION DATA |
| title_sort | comparing the performance of roof segmentation methods in an urban environment using digital elevation data |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W1-2022/165/2023/isprs-annals-X-4-W1-2022-165-2023.pdf |
| work_keys_str_mv | AT bfarajelahi comparingtheperformanceofroofsegmentationmethodsinanurbanenvironmentusingdigitalelevationdata AT mnajaf comparingtheperformanceofroofsegmentationmethodsinanurbanenvironmentusingdigitalelevationdata AT harefi comparingtheperformanceofroofsegmentationmethodsinanurbanenvironmentusingdigitalelevationdata |