Automated 3D Building Model Reconstruction using Orthophotos and Point Clouds
The accurate reconstruction of 3D building models is essential for urban planning and smart city applications. This study introduces an automated workflow integrating Unmanned Aerial Vehicle (UAV)-derived orthophotos and point clouds to enhance reconstruction accuracy. A deep learning-based tree seg...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-07-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/233/2025/isprs-archives-XLVIII-G-2025-233-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The accurate reconstruction of 3D building models is essential for urban planning and smart city applications. This study introduces an automated workflow integrating Unmanned Aerial Vehicle (UAV)-derived orthophotos and point clouds to enhance reconstruction accuracy. A deep learning-based tree segmentation model filters non-building objects, while the Cloth Simulation Filter (CSF) separates ground and non-ground points. Clustering techniques isolate building structures, followed by Mobile Line Segment Detector (LSD)-based roof edge detection and refinement. The extracted roof edges are then combined with height attributes to generate 3D bounding boxes. Experiments on UAV data from Suseo, South Korea, show that this approach reconstructs detailed and realistic 3D models, achieving high precision and recall measures. By integrating deep learning, clustering, and geometric analysis, this study provides a scalable and efficient solution for urban modeling. |
|---|---|
| ISSN: | 1682-1750 2194-9034 |