期刊论文

Li, Chang

英文

Canadian Journal of Remote Sensing

0703-8992

2021

47

3

396-412

10.1080/07038992.2021.1944802

The authors are grateful for the comments and contributions of the editors, anonymous reviewers and the members of the editorial team. This work was supported by the [National Natural Science Foundation of China (NSFC)] under Grant [41771493], [41101407]; [Wuhan Youth Science and Technology Plan] under Grant [2016070204010137]; [National Key Research and Development Program] under Grant [2016YFC0503506]; [Hubei Soil and Water Conservation Monitoring Research]; and [self-determined research funds of CCNU from the basic research and operation of MOE] under Grant [CCNU19TS002].

本校为第一机构

Land use and land cover (LULC) mapping is a basic research topic in geography. In deep-learning (DL)-based LULC mapping, there are primarily the following issues: training and testing samples for DL are typically annotated by indoor visual interpretation without field surveys; remotely sensed scene classification based on DL typically lacks fine geometric boundaries for ground objects; historical big data (HBD) (e.g., vector data) are underutilized in DL; studies of large-scale remote sensing mapping (LSRSM) using DL are rare. To solve above is...