TY  - Data
T1  - Long time-series glacier profile dataset for the Sanjiangyuan area (1986-2021)
A1  - None
DO  - 10.5281/zenodo.5512064
PY  - 2023
DA  - 2023-08-29
PB  - National Cryosphere Desert Data Center
AB  - &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.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/791599d6-8d0b-4dd7-8bb0-a9b35e031a45
ER  -