Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"

Glacial lake dataset for the paper "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset" (https://doi.org/10.1016/j.jhydrol.2025.133072)

The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.

GLID.rar contains the training dataset (16,000 samples),  and val.zip contains the validation dataset (2,367 samples).

GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.

Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.

Transferability validation.zip contains the images, labels, and predictions for transferability validation, which is independent of GLID (not used for model training or validation). The file structure is shown below:

Transferability validation.zip

• images
  • AS.tif
  • GL.tif
  • NA.tif
  • SA.tif
• labels
  • AS_gt.tif
  • GL_gt.tif
  • NA_gt.tif
  • SA_gt.tif
• predictions
  • AS_pred.tif
  • GL_pred.tif
  • NA_pred.tif
  • SA_pred.tif

AS, GL, NA, and SA represent Asia, Greenland, North America, and South America, respectively. Four high-quality Landsat-8/9 images (each cloud cover less than 6%) were used for testing, and we manually annotated the glacial lakes in each image as labels. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.  Files in transferability validation.zip have a same CRS of WGS 84.

If you find this dataset is helpful in your research, please consider cite this paper:

Ma D, Li J, Jiang L. 2025. Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset. Journal of Hydrology 657: 133072.