Zero-shot building footprint extraction and regularization based on Segment Anything model with Mesh Model
With the advancement of urbanization, building footprint data plays an important role in urban planning, 3D Real Scene and smart cities. Traditional manual contouring methods are time-consuming and laborious, while deep learning-based building extraction methods often require a large amount of label...
Saved in:
| Main Authors: | J. Zhong, Y. Zhang, X. Liu, J. Zhang, L. Fei, W. Xia, B. Zhang, W. Fan, D. Yue |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1777/2025/isprs-archives-XLVIII-G-2025-1777-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
IDCC-SAM: A Zero-Shot Approach for Cell Counting in Immunocytochemistry Dataset Using the Segment Anything Model
by: Samuel Fanijo, et al.
Published: (2025-02-01) -
Efficient microstructure segmentation in three-dimensional imaging: Combining few-shot learning with the segment anything modelEarth/Chem
by: Po-Yen Tung, et al.
Published: (2025-07-01) -
Segment Anything Model with LiDAR based images for building segmentation in forest areas
by: E. Hattula, et al.
Published: (2025-05-01) -
SEMPNet: enhancing few-shot remote sensing image semantic segmentation through the integration of the segment anything model
by: Wei Ao, et al.
Published: (2024-12-01) -
TPP-SAM: A Trajectory Point Prompting Segment Anything Model for Zero-Shot Road Extraction From High-Resolution Remote Sensing Imagery
by: Tao Wu, et al.
Published: (2025-01-01)