Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data
This work addresses the need for efficient urban surveying to support large-scale urban management and sustainable development, though it is incremental as it applies a semi-supervised framework to existing data types.
The study tackled the problem of identifying building functions in large-scale urban areas using multi-modality remote-sensing data, achieving an overall accuracy of 82% and a Kappa of 71% for 1,616,796 buildings in Shanghai, China.
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.