15 Feb 2023
 | 15 Feb 2023
Status: a revised version of this preprint is currently under review for the journal TC.

Refined glacial lake extraction in high Asia region by Deep Neural Network and Superpixel-based Conditional Random Field

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

Abstract. Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst disasters. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. Therefore, this study constructed a high-resolution dataset containing seven types of glacial lakes and proposed a refined glacial lake extraction method, which combines the LinkNet50 Network for rough extraction and Simple Linear Iterative Clustering (SLIC)-Dense Conditional Random Field (DenseCRF) for optimization. The results show that: 1) With Google Earth images of 0.52 m resolution in the study area, the Recall, Precision, F1 Score, and IoU of glacial lake extraction based on the proposed method are 96.52 %, 92.49 %, 94.46 %, and 90.69 %, respectively. 2) With the Google Earth images of 2.11 m resolution in the Qomolangma National Nature Reserve, 2300 glacial lakes with a total area of about 65.17 km2 were detected by the proposed method. The area of the minimum glacial lake that can be extracted is about 160 m2 (6×6 pixels). This method has advantages in small glacial lake extraction and refined outline detection, which can be applied to extracting glacial lakes in the high Asia region with high-resolution images.

Yungang Cao et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-267', Anonymous Referee #1, 18 Apr 2023
    • AC1: 'Reply on RC1', Yungang Cao, 10 Aug 2023
  • RC2: 'Comment on tc-2022-267', Connor Shiggins, 14 Jul 2023
    • AC2: 'Reply on RC2', Yungang Cao, 10 Aug 2023

Yungang Cao et al.

Yungang Cao et al.


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Short summary
This study built a glacial lake dataset with 15376 samples in 7 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 achieves the best results so far, in which the F1 Score and IoU reach 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.