%0 Dataset
%T Long time-series glacier profile dataset for the Sanjiangyuan area (1986-2021)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/791599d6-8d0b-4dd7-8bb0-a9b35e031a45
%W NCDC
%R 10.5281/zenodo.5512064
%A None
%K glacier outline;Glacier area;Space-time glacial change
%X &Emsp; Deep learning-based methods have attracted great attention in glacier extraction due to their advantages over traditional techniques. In this study, we verified the feasibility and effectiveness of LandsNet architecture in glacier extraction, and we applied the improved LandsNet (M-LandsNet) to extract the glacier contours in the headwaters of the Three Rivers. Two scenarios were compared using the band ratio method, U-Net, U-Net++, GlacierNet, SaU-Net, U-Net+cSE and LandsNet. The analysis of the two scenarios shows that M-LandsNet has the best performance and generalization ability among the 1986 methods.