期刊论文

Wu, Yijin;Li, C

英文

Remote Sensing

2072-4292

2023

15

15

3907-

10.3390/rs15153907

Conceptualization, P.Z. and C.L.; Methodology, P.Z. and C.L.; Software, G.Z. and D.L.; Formal analysis, P.Z.; Resources, Y.W., R.L., H.Y. and Y.Z.; Data curation, Y.W., H.Y. and Y.Z.; Writing—original draft, P.Z.; Writing—review & editing, P.Z. and C.L.; Visualization, G.Z. and D.L.; Supervision, Y.W., C.L. and R.L.; Project administration, Y.W.; Funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) (Grant No. 41771493 and 41101407) and the Fundamental Research Funds for the Central Universities (Grant No. CCNU22QN019).

本校为第一且通讯机构

A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first ...