Generating viewsheds based on the Digital Surface Model (DSM) and point cloud.
Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0312146 |
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| author | Jerzy Orlof Paweł Ozimek Piotr Łabędź Adrian Widłak Agnieszka Ozimek |
| author_facet | Jerzy Orlof Paweł Ozimek Piotr Łabędź Adrian Widłak Agnieszka Ozimek |
| author_sort | Jerzy Orlof |
| collection | DOAJ |
| description | Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions. |
| format | Article |
| id | doaj-art-6e094a44d96741b393fb7976a78e46e4 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-6e094a44d96741b393fb7976a78e46e42025-01-08T05:32:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031214610.1371/journal.pone.0312146Generating viewsheds based on the Digital Surface Model (DSM) and point cloud.Jerzy OrlofPaweł OzimekPiotr ŁabędźAdrian WidłakAgnieszka OzimekVisual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions.https://doi.org/10.1371/journal.pone.0312146 |
| spellingShingle | Jerzy Orlof Paweł Ozimek Piotr Łabędź Adrian Widłak Agnieszka Ozimek Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. PLoS ONE |
| title | Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. |
| title_full | Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. |
| title_fullStr | Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. |
| title_full_unstemmed | Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. |
| title_short | Generating viewsheds based on the Digital Surface Model (DSM) and point cloud. |
| title_sort | generating viewsheds based on the digital surface model dsm and point cloud |
| url | https://doi.org/10.1371/journal.pone.0312146 |
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