Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910438842695680 |
|---|---|
| author | Li, Zhuohong He, Wei Li, Jiepan Zhang, Hongyan |
| author_facet | Li, Zhuohong He, Wei Li, Jiepan Zhang, Hongyan |
| contents | Buildings, as fundamental man-made structures in urban environments, serve as crucial indicators for understanding various city function zones. Rapid urbanization has raised an urgent need for efficiently surveying building footprints and functions. In this study, we proposed a semi-supervised framework to identify every building's function in large-scale urban areas with multi-modality remote-sensing data. In detail, optical images, building height, and nighttime-light data are collected to describe the morphological attributes of buildings. Then, the area of interest (AOI) and building masks from the volunteered geographic information (VGI) data are collected to form sparsely labeled samples. Furthermore, the multi-modality data and weak labels are utilized to train a segmentation model with a semi-supervised strategy. Finally, results are evaluated by 20,000 validation points and statistical survey reports from the government. The evaluations reveal that the produced function maps achieve an OA of 82% and Kappa of 71% among 1,616,796 buildings in Shanghai, China. This study has the potential to support large-scale urban management and sustainable urban development. All collected data and produced maps are open access at https://github.com/LiZhuoHong/BuildingMap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05133 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data Li, Zhuohong He, Wei Li, Jiepan Zhang, Hongyan Computer Vision and Pattern Recognition Image and Video Processing Buildings, as fundamental man-made structures in urban environments, serve as crucial indicators for understanding various city function zones. Rapid urbanization has raised an urgent need for efficiently surveying building footprints and functions. In this study, we proposed a semi-supervised framework to identify every building's function in large-scale urban areas with multi-modality remote-sensing data. In detail, optical images, building height, and nighttime-light data are collected to describe the morphological attributes of buildings. Then, the area of interest (AOI) and building masks from the volunteered geographic information (VGI) data are collected to form sparsely labeled samples. Furthermore, the multi-modality data and weak labels are utilized to train a segmentation model with a semi-supervised strategy. Finally, results are evaluated by 20,000 validation points and statistical survey reports from the government. The evaluations reveal that the produced function maps achieve an OA of 82% and Kappa of 71% among 1,616,796 buildings in Shanghai, China. This study has the potential to support large-scale urban management and sustainable urban development. All collected data and produced maps are open access at https://github.com/LiZhuoHong/BuildingMap. |
| title | Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.05133 |