The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data  

作　　者：ZHANG Xinchang SHI Qian SUN Ying HUANG Jianfeng HE Da 

机构地区：[1]College of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China [2]School of Geography and Remote Sensing,Guangzhou University,Guangzhou 510006,China [3]School of Geography and Planning,Sun Yat-sen University,Guangzhou 510006,China [4]School of Atmospheric Sciences,Sun Yat-sen University,Zhuhai 519082,China

出　　处：《Journal of Geodesy and Geoinformation Science》2024年第3期1-23,共23页测绘学报（英文版）

基　　金：National Natural Science Foundation of China(Nos.42371406,42071441,42222106,61976234).

摘　　要：With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

关 键 词：remote sensing big data deep learning semantic segmentation land use/land cover mapping 

分 类 号：P237[天文地球—摄影测量与遥感]