Estimating building height using scene classification and spatial geometry
Building height significantly influences urban development and evolution. Previous studies on building height estimation using digital surface models (DSMs) have predominantly addressed simple, single-environmental scenarios, often yielding unsatisfactory results across diverse environments. This st...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322500322X |
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| author | Yonghua Jiang Jingxin Chang Yunming Wang Shaodong Wei Deren Li |
| author_facet | Yonghua Jiang Jingxin Chang Yunming Wang Shaodong Wei Deren Li |
| author_sort | Yonghua Jiang |
| collection | DOAJ |
| description | Building height significantly influences urban development and evolution. Previous studies on building height estimation using digital surface models (DSMs) have predominantly addressed simple, single-environmental scenarios, often yielding unsatisfactory results across diverse environments. This study introduces a novel method for estimating building height by integrating scene classification with spatial geometric relationships. Initially, raw data are processed to derive the various data types required for this approach. Environmental scene classification, based on vegetation and shadows analysis, is then performed. Subsequently, the building height is estimated either directly from the DSM or through road height prediction. The proposed method is validated using a scene image from Wuhan, Hubei Province, China. The results demonstrate that the estimated building height maintains high accuracy in complex environments with significant vegetation and shadow coverage, achieving a mean absolute error of 1.84 m. Furthermore, the proposed method outperforms existing DSM-based techniques. This approach is adaptable for high-precision building height estimation across various environments and holds substantial application potential, facilitating further research in urban-related scenarios. |
| format | Article |
| id | doaj-art-336c0b4faa374212b3e5d77c5cf3dafa |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-336c0b4faa374212b3e5d77c5cf3dafa2025-08-20T02:36:23ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-07-0114110467510.1016/j.jag.2025.104675Estimating building height using scene classification and spatial geometryYonghua Jiang0Jingxin Chang1Yunming Wang2Shaodong Wei3Deren Li4School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; Corresponding author at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaBuilding height significantly influences urban development and evolution. Previous studies on building height estimation using digital surface models (DSMs) have predominantly addressed simple, single-environmental scenarios, often yielding unsatisfactory results across diverse environments. This study introduces a novel method for estimating building height by integrating scene classification with spatial geometric relationships. Initially, raw data are processed to derive the various data types required for this approach. Environmental scene classification, based on vegetation and shadows analysis, is then performed. Subsequently, the building height is estimated either directly from the DSM or through road height prediction. The proposed method is validated using a scene image from Wuhan, Hubei Province, China. The results demonstrate that the estimated building height maintains high accuracy in complex environments with significant vegetation and shadow coverage, achieving a mean absolute error of 1.84 m. Furthermore, the proposed method outperforms existing DSM-based techniques. This approach is adaptable for high-precision building height estimation across various environments and holds substantial application potential, facilitating further research in urban-related scenarios.http://www.sciencedirect.com/science/article/pii/S156984322500322XBuilding heightDigital surface modelScene classificationSpatial geometric relationshipsElevation prediction |
| spellingShingle | Yonghua Jiang Jingxin Chang Yunming Wang Shaodong Wei Deren Li Estimating building height using scene classification and spatial geometry International Journal of Applied Earth Observations and Geoinformation Building height Digital surface model Scene classification Spatial geometric relationships Elevation prediction |
| title | Estimating building height using scene classification and spatial geometry |
| title_full | Estimating building height using scene classification and spatial geometry |
| title_fullStr | Estimating building height using scene classification and spatial geometry |
| title_full_unstemmed | Estimating building height using scene classification and spatial geometry |
| title_short | Estimating building height using scene classification and spatial geometry |
| title_sort | estimating building height using scene classification and spatial geometry |
| topic | Building height Digital surface model Scene classification Spatial geometric relationships Elevation prediction |
| url | http://www.sciencedirect.com/science/article/pii/S156984322500322X |
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