ABSTRACT
Satellite derived Urban green space (UGS) maps provide an efficient and effective tool for urban studies and contribute to targets and indicators of the sustainable development goal. However, remote sensing of mapping UGS is challenging due to the existence of mixed pixels and the cost and difficulty of collecting quality training data. This chapter presents a crowdsourced data driven GeoAI model training for mapping UGS from Sentinel-2A satellite images. The proposed GeoAI method consists of three parts: 1) a multi-scale feature extraction module; 2) a multi-modal information fuse module; and 3) and a boundary enhancement module. The results showed that the proposed GeoAI achieved a high overall classification accuracy of 94.6%, which presents a clear UGS structure of a large scale. This chapter provide a fresh insight into how remote sensing and crowdsourced geospatial big data can be integrated to improve urban mapping of green spaces.
