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

被引：0

作者：

Liu, Mengxi ^{[1
]}

Li, Jianlong ^{[1
]}

Li, Zeteng ^{[1
]}

Shi, Qian ^{[1
]}

机构：

[1] Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Peoples R China

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

Urban green space; deep learning; convolutional nerual network; high-resolution images;

D O I：

10.1109/IGARSS46834.2022.9884665

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：6029 / 6032

页数：4

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