A Practical Framework for Estimating Façade Opening Rates of Rural Buildings Using Real-Scene 3D Models Derived from Unmanned Aerial Vehicle Photogrammetry
The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/9/1596 |
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| Summary: | The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas. |
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| ISSN: | 2072-4292 |