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Training polygons for mapping retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau)

The shapefile contains 354 polygons which are boundaries of retrogressive thaw slumps (RTSs) and other land covers (non-RTS) in Beiluhe on the Tibetan Plateau for training a deep learning algorithm (DeepLabv3+). Among them, 264 are RTS boundaries delineated on Planet images acquired in May 2018, 90 of them are non-RTS polygons. In the attribute table of the shapefile, "class_int" equal to "1" means an RTS polygon and "0" for a non-RTS polygon.

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Cite this as

Huang, Lingcao, Luo, Jing, Lin, Zhanju, Niu, Fujun, Liu, Lin (2019). Dataset: Training polygons for mapping retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau). https://doi.org/10.1594/PANGAEA.908909

DOI retrieved: 2019

Additional Info

Field Value
Imported on November 29, 2024
Last update November 29, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.908909
Author Huang, Lingcao
Given Name Lingcao
Family Name Huang
More Authors
Luo, Jing
Lin, Zhanju
Niu, Fujun
Liu, Lin
Source Creation 2019
Publication Year 2019
Subject Areas
Name: Ecology

Name: HumanDimensions

Name: LandSurface

Related Identifiers
Title: Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images
Identifier: https://doi.org/10.1016/j.rse.2019.111534
Type: DOI
Relation: IsSupplementTo
Year: 2020
Source: Remote Sensing of Environment
Authors: Huang Lingcao , Luo Jing , Lin Zhanju , Niu Fujun , Liu Lin .