Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds
Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammet...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9507433/ |
| _version_ | 1819320547905896448 |
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| author | Liang Guo Xingdong Deng Yang Liu Huagui He Hong Lin Guangxin Qiu Weijun Yang |
| author_facet | Liang Guo Xingdong Deng Yang Liu Huagui He Hong Lin Guangxin Qiu Weijun Yang |
| author_sort | Liang Guo |
| collection | DOAJ |
| description | Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities. |
| first_indexed | 2024-12-24T11:21:19Z |
| format | Article |
| id | doaj.art-eceb03c9a311492795bb003fed8d2062 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T11:21:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eceb03c9a311492795bb003fed8d20622022-12-21T16:58:14ZengIEEEIEEE Access2169-35362021-01-01911182311183210.1109/ACCESS.2021.31026329507433Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point CloudsLiang Guo0Xingdong Deng1https://orcid.org/0000-0001-5776-8246Yang Liu2https://orcid.org/0000-0003-3398-0255Huagui He3Hong Lin4Guangxin Qiu5Weijun Yang6Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaPoint clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.https://ieeexplore.ieee.org/document/9507433/PhotogrammetryLiDARbuilding extractiondigital surface modeldifference of normals |
| spellingShingle | Liang Guo Xingdong Deng Yang Liu Huagui He Hong Lin Guangxin Qiu Weijun Yang Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds IEEE Access Photogrammetry LiDAR building extraction digital surface model difference of normals |
| title | Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds |
| title_full | Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds |
| title_fullStr | Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds |
| title_full_unstemmed | Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds |
| title_short | Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds |
| title_sort | extraction of dense urban buildings from photogrammetric and lidar point clouds |
| topic | Photogrammetry LiDAR building extraction digital surface model difference of normals |
| url | https://ieeexplore.ieee.org/document/9507433/ |
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