Articles | Volume 18, issue 1
https://doi.org/10.5194/tc-18-153-2024
https://doi.org/10.5194/tc-18-153-2024
Research article
 | 
08 Jan 2024
Research article |  | 08 Jan 2024

Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods

Yungang Cao, Rumeng Pan, Meng Pan, Ruodan Lei, Puying Du, and Xueqin Bai

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Discipline: Glaciers | Subject: Remote Sensing
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Cited articles

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Berman, M., Triki, A. R., and Blaschko, M. B.: The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks, Proceedings of the IEEE conference on computer vision and pattern recognition, 18–22 June 2018, Salt Lake City, United States, 4413–4421, https://doi.org/10.1109/CVPR.2018.00464, 2018. 
Bourgois, J., Cisternas, M. E., Braucher, R., Bourlès, D., and Frutos, J.: Geomorphic records along the general Carrera (Chile)–Buenos Aires (Argentina) glacial lake (46–48 S), climate inferences, and glacial rebound for the past 7–9 ka, J. Geol., 124, 27–53, https://doi.org/10.1086/684252, 2016. 
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Short summary
This study built a glacial lake dataset with 15376 samples in seven types and proposed an automatic method by two-stage (the semantic segmentation network and post-processing) optimizations to detect glacial lakes. The proposed method for glacial lake extraction has achieved the best results so far, in which the F1 score and IoU reached 0.945 and 0.907, respectively. The area of the minimum glacial lake that can be entirely and correctly extracted has been raised to the 100 m2 level.