Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data

TL;DR

This paper presents a semi-supervised method using multi-modality remote sensing data to accurately identify building functions across large urban areas, aiding urban management and planning.

Contribution

It introduces a novel semi-supervised framework that combines optical, height, and nighttime-light data with weak labels for large-scale building function mapping.

Findings

Achieved 82% overall accuracy and 71% Kappa on Shanghai data.

Successfully mapped functions for over 1.6 million buildings.

Demonstrated potential for supporting urban planning and sustainable development.

Abstract

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.