A DEEP LEARNING METHOD FOR FINED-GRAINED URBAN GREEN SPACE MAPPING

In view of the challenges on urban green space (UGS) mapping from high-resolution images (HRIs), including insufficient dataset as well as the intra-class difference and interclass similarity of UGS in HRIs, we propose a novel network for UGS extraction (UGSNet) and collect an large urban green space dataset (UGSet) with 4,454 samples of size 512x512 in this paper. The UGSNet integrates the attention mechanism to improve the discrimination of UGS, and employs a point head with point rending strategy for precise edge recovery. Comparison experiments with the state-of-the-art (SOTA) semantic segmentation models show that the UGSNet can achieve the highest F1 of 77.30% on UGSet.