  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.
