Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method
With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and...
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MDPI AG
2025-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| author | Cheng Zhang Yiwen Wang Haozhe Cheng Wanfeng Dou |
| author_facet | Cheng Zhang Yiwen Wang Haozhe Cheng Wanfeng Dou |
| author_sort | Cheng Zhang |
| collection | DOAJ |
| description | With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, including topological relationships, metric relationships, etc. This study proposes a novel method of viewshed analysis, which solves the limitation of treating the viewshed as a unified unit in traditional viewshed analysis by decomposing the viewshed into multiple viewsheds and quantifying their spatial semantic relationships. The method uses a DBSCAN clustering algorithm with terrain adaptability to divide a viewshed into spatially different viewsheds and characterizes these viewsheds through a systematic measurement framework, including azimuth, area, and sparsity. The method was applied to a case study of Purple Mountain in Nanjing. The experiment used 12.5 m accuracy topographic data from Purple Mountain, and two observation points were selected. For the first observation point near the mountain park, during the DBSCAN clustering partition of the viewshed, the number of clusters and the number of noise points were compared with determine the neighborhood radius of 18 m and the minimum sample point number of 4. Five viewsheds were successfully generated, with the largest viewshed having 468 visible points and the smallest only 16, located in different locations from the observer, reflecting the spatial variability of terrain features. All viewsheds are basically distributed to the north of the observer, two of which also share the northeast 87° direction with the observer in a straight line distribution but at different distances. In three-dimensional space, the distance between the two viewsheds is 317.298 m. Azimuth angle verification showed significant aggregation in the northeast direction. The second point is near the ridgeline, where one viewshed accounts for 87.52% of the total viewshed, showing significant visual effects. One viewshed is 3121.113 m away from the observer, with only 113 visible points, and is not located at a low altitude, so it is suitable for a long-distance fixed-point intermittent observation. The experimental results of the two observation points reveal the directional dominance and distance stratification of viewshed spatial relationships. This paper proposes a model to express topographical viewshed spatial relationships. The model analyzes and describes the spatial features of the viewshed through quantitative and qualitative methods. These metric features provide a basis for constructing spatial topological relationships between observation points and viewsheds, helping optimize viewpoint selection and enhance landscape planning. Compared with traditional methods, the proposed method significantly improves the resolution of spatial semantic relationship expression and has practical application value in fields such as archaeology, tourism planning, and urban design. |
| format | Article |
| id | doaj-art-29d242dae16a4114b51fbfbb76b8663f |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-03-01 |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-29d242dae16a4114b51fbfbb76b8663f2025-08-20T02:11:05ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-03-0114311310.3390/ijgi14030113Spatial Semantic Expression of Terrain Viewshed: A Data Mining MethodCheng Zhang0Yiwen Wang1Haozhe Cheng2Wanfeng Dou3Digital Construction Center, Jiangsu Open University, Nanjing 210036, ChinaSchool of Geography, Nanjing Normal University, Nanjing 210023, ChinaSchool of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, ChinaSchool of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, ChinaWith the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, including topological relationships, metric relationships, etc. This study proposes a novel method of viewshed analysis, which solves the limitation of treating the viewshed as a unified unit in traditional viewshed analysis by decomposing the viewshed into multiple viewsheds and quantifying their spatial semantic relationships. The method uses a DBSCAN clustering algorithm with terrain adaptability to divide a viewshed into spatially different viewsheds and characterizes these viewsheds through a systematic measurement framework, including azimuth, area, and sparsity. The method was applied to a case study of Purple Mountain in Nanjing. The experiment used 12.5 m accuracy topographic data from Purple Mountain, and two observation points were selected. For the first observation point near the mountain park, during the DBSCAN clustering partition of the viewshed, the number of clusters and the number of noise points were compared with determine the neighborhood radius of 18 m and the minimum sample point number of 4. Five viewsheds were successfully generated, with the largest viewshed having 468 visible points and the smallest only 16, located in different locations from the observer, reflecting the spatial variability of terrain features. All viewsheds are basically distributed to the north of the observer, two of which also share the northeast 87° direction with the observer in a straight line distribution but at different distances. In three-dimensional space, the distance between the two viewsheds is 317.298 m. Azimuth angle verification showed significant aggregation in the northeast direction. The second point is near the ridgeline, where one viewshed accounts for 87.52% of the total viewshed, showing significant visual effects. One viewshed is 3121.113 m away from the observer, with only 113 visible points, and is not located at a low altitude, so it is suitable for a long-distance fixed-point intermittent observation. The experimental results of the two observation points reveal the directional dominance and distance stratification of viewshed spatial relationships. This paper proposes a model to express topographical viewshed spatial relationships. The model analyzes and describes the spatial features of the viewshed through quantitative and qualitative methods. These metric features provide a basis for constructing spatial topological relationships between observation points and viewsheds, helping optimize viewpoint selection and enhance landscape planning. Compared with traditional methods, the proposed method significantly improves the resolution of spatial semantic relationship expression and has practical application value in fields such as archaeology, tourism planning, and urban design.https://www.mdpi.com/2220-9964/14/3/113terrain viewshed analysisviewshed topological structureviewshed spatial semantic informationfeature extractioncluster analysis |
| spellingShingle | Cheng Zhang Yiwen Wang Haozhe Cheng Wanfeng Dou Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method ISPRS International Journal of Geo-Information terrain viewshed analysis viewshed topological structure viewshed spatial semantic information feature extraction cluster analysis |
| title | Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method |
| title_full | Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method |
| title_fullStr | Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method |
| title_full_unstemmed | Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method |
| title_short | Spatial Semantic Expression of Terrain Viewshed: A Data Mining Method |
| title_sort | spatial semantic expression of terrain viewshed a data mining method |
| topic | terrain viewshed analysis viewshed topological structure viewshed spatial semantic information feature extraction cluster analysis |
| url | https://www.mdpi.com/2220-9964/14/3/113 |
| work_keys_str_mv | AT chengzhang spatialsemanticexpressionofterrainviewshedadataminingmethod AT yiwenwang spatialsemanticexpressionofterrainviewshedadataminingmethod AT haozhecheng spatialsemanticexpressionofterrainviewshedadataminingmethod AT wanfengdou spatialsemanticexpressionofterrainviewshedadataminingmethod |