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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1569-8432 |