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...
| Main Authors: | , , , , |
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| Format: | Dataset |
| Language: | English |
| Published: |
PANGAEA
2019
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.1594/pangaea.908909 https://doi.pangaea.de/10.1594/PANGAEA.908909 |
| Summary: | 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. ... : Supplement to: Huang, Lingcao; Luo, Jing; Lin, Zhanju; Niu, Fujun; Liu, Lin (2020): Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images. Remote Sensing of Environment, 237, 111534 ... |
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