| 09 Jul 2026
Glacial-Lake-Bench: A Global Multi-Sensor Benchmark Dataset for Evaluating Deep Learning Models for Glacial Lake Mapping
Abstract. Glacial lakes are among the most sensitive indicators of climate change, closely linked to natural hazards and are natural reservoirs of freshwater resources. Thus, automated mapping and monitoring of glacial lakes is imperative. However, most automated approaches either remain regional in scope or show limited performance under challenging conditions such as cloud cover, shadows, and spatially small or frozen lakes. The global scale analysis and comparative evaluation of deep learning models is primarily hindered by the lack of readily available datasets for training data. To address this gap, we present Glacial Lake-Bench (GLB), a multisource remote sensing dataset comprising Sentinel-2, Sentinel-1, and Copernicus DEM-derived terrain (11 channels in total). GLB consists of 19,115 image-label pairs (256x256x11) spanning all Randolph Glacier Inventory (RGI) regions except Antarctica, providing the first-ever global, multi-sensor dataset for glacial lake segmentation. In addition, we compiled Glacial Lake-Bench-Challenge (GLBC), a curated subset of 1,105 image-label pairs representing scenes with cloud cover, shadow, frozen lake surfaces, and small lakes to establish a community standard for evaluating model robustness under difficult conditions. Labels are derived from Zhang et al. (2024); we independently quantify label quality through stratified sampling of 50 image-label pairs from each RGI region, amounting to 900 chips in total. Our quality assessment reveals a mean Intersection over Union (mIoU) of 0.95, precision of 0.99, recall of 0.96, and per-region agreement that is consistent with the known difficulty of small, turbid, shadowed lakes in high-mountain terrain. To demonstrate that the dataset is usable, well-posed, and appropriately challenging, we provide reference baselines from two convolutional networks (U-Net, DeepLabv3+) and two Geo-Foundation Models (GFMs) (DOFA, Prithvi-EO-2.0), evaluated with a recommended leave-one-region-out (LORO) protocol that minimizes spatial autocorrelation, alongside a random split and the GLBC subset. Baseline mIoU reaches 0.80–0.85 on GLB and drops to 0.74–0.79 on GLBC, confirming that the challenge subset isolates genuinely difficult conditions. The GLB dataset is available at https://zenodo.org/records/17917359 (Kaushik, 2026)
General comments:
Overall, this is a well-designed and insightful study that makes a valuable contribution to the field of glacial lake mapping. The proposed global glacial lake dataset is particularly meaningful because it helps establish a community benchmark for evaluating model robustness under challenging mapping conditions. I also appreciate the development of the Glacial Lake-Bench-Challenge (GLBC) dataset, which is designed to address several key difficulties in glacial lake mapping, including cloud cover, shadows, frozen surfaces, and small lake size.
One point that would benefit from further clarification concerns the role of the 11-band input in improving small-lake detection. I understand that the SAR band can facilitate glacial lake mapping under cloud-cover conditions. However, it remains unclear how the full set of input bands specifically enhances the detection of small glacial lakes. It would be helpful if the authors could clarify the underlying mechanisms or provide additional evidence, for example by explaining whether these bands improve spectral separability, boundary delineation, or model sensitivity to small water bodies.
A second aspect concerns the evaluation of model generalization. The model demonstrates good spatial transferability, likely benefiting from the globally distributed labels. To further strengthen the evaluation, I suggest adding evidence of the model’s temporal transferability, for example by testing images acquired in years other than those used for training or validation. In addition, a more detailed discussion of the imbalanced model performance across different regions would be valuable. Potential factors may include regional differences in lake morphology, image quality, cloud and shadow conditions, training-sample distribution, and background complexity. Discussing these factors would help readers better understand the limitations of the current model and possible directions for future improvement.
Finally, beyond serving as a baseline dataset for assessing newly proposed models, the GLB dataset has strong potential to function as a high-quality and representative training resource for developing more advanced glacial lake mapping models. I encourage the authors to discuss this possible application more explicitly, particularly how the dataset’s global coverage, challenging scenarios, and annotation quality may support future model training, comparison, and benchmarking.
Specific comments:
Line 24: The statement “Labels are derived from Zhang et al. (2024)” is somewhat ambiguous. Please clarify whether the labels were directly adopted from Zhang et al. (2024), manually delineated in the present study, or further revised based on that reference dataset. A brief description of the label-generation procedure would improve transparency and reproducibility.
Lines 65–83: The literature review would be strengthened by including additional studies on deep-learning-based glacial lake mapping. The following works may be relevant and could help better position the contribution of the present study within the existing body of research:
Qayyum, N., Ghuffar, S., Ahmad, H.M., Yousaf, A., Shahid, I., 2020. Glacial lakes mapping using multi satellite PlanetScopeScope imagery and deep learning. ISPRS Int. J. Geo-Inf. 9 (10), 560.
Wu, R., Liu, G., Zhang, R., Wang, X., Li, Y., Zhang, B., et al., 2020. A deep learning method for mapping glacial lakes from the combined use of synthetic-aperture radar and optical satellite images. Rem. Sens. 12 (24), 4020.
Chen, F., 2021. Comparing methods for segmenting supra-glacial lakes and surface features in the mount everest region of the himalayas using chinese gaofen-3 sar images. Rem. Sens. 13 (13), 2429.
Xu, X., Liu, L., Huang, L., Hu, Y., 2024. Combined use of multi-source satellite imagery and deep learning for automated mapping of glacial lakes in the Bhutan Himalaya. Sci. Remote Sens. 10, 100157.
Line 95: Please specify the minimum mapped lake size for both the Glacial Lake-Bench (GLB) dataset and the Glacial Lake-Bench-Challenge (GLBC) dataset. Clarifying whether the two datasets use the same or different minimum-size thresholds would help readers better understand their comparability and intended applications.
Lines 126–128: Since the glacial lake boundaries were obtained from Zhang et al. (2024), while the 2020 Sentinel-2 images were collected independently in this study, please explain how potential inconsistencies between the labels and image acquisition dates were addressed. For example, were the lake boundaries visually checked or adjusted to match the 2020 imagery, and how were cases of lake expansion, shrinkage, or disappearance handled?
Lines 126–128: Please also clarify the specific months defined as the ablation season. Because the timing of the ablation season varies substantially across glaciated regions worldwide, it would be useful to explain whether a uniform time window was applied globally or whether region-specific seasonal windows were used.
Line 132: Please clarify what is meant by the “specified date range” in this context. If the date range does not correspond exactly to 2020, please indicate which year or time period the labels represent and how this relates to the 2020 imagery used in the analysis.
Lines 178–190: The spatial coverage of the GLB dataset appears to vary substantially among regions, ranging from 9.21% to 89.42%. Please explain the main reasons for this large regional variation. For example, is it primarily caused by cloud-cover conditions during image selection, by imbalanced regional data coverage inherited from Zhang et al. (2024), or by other factors such as image availability, terrain complexity, or quality-control criteria?
Section 2.4: Please provide more information on the time and labor required for the manual correction process. For instance, it would be helpful to report the approximate number of annotators involved, the total or average correction time, and whether any quality-control or cross-checking procedures were applied.
Figure 4: Please clarify whether the term “boundary” in the upper legend refers to the “adjusted boundary.” If so, using consistent terminology between the figure legend and the main text would help avoid confusion.
Figure 5: In the upper-right example, the manually corrected boundary appears not to align well with the background image. Please check whether this is due to a visualization offset, image–label misregistration, or an error in the displayed boundary, and revise the figure or explanation if necessary.
Line 257: Please clarify the input configuration used for the DeepLabv3+ model. Does DeepLabv3+ require a three-band input in this study, and if so, were three bands selected from the previously stacked 11-band input? If only a subset of bands was used, please specify which bands were selected and explain the rationale.
Figure 8: If space allows, please add the corresponding scenario labels to the images in the first column. This would make the figure easier to interpret and help readers more directly compare model performance across different challenging conditions.
Line 313: Because the performance of the DeepLabv3+ model appears broadly comparable to that of the other models, rather than substantially worse, please consider adding leave-one-region-out cross-validation (LORO) results for DeepLabv3+ as well. Including this comparison would make the experimental evaluation more consistent across model architectures and would provide a fairer assessment of spatial transferability.
Figure 11: Please clarify which scenario Figure 11(i) is intended to represent. Adding a label or brief explanation in the caption would help readers understand the purpose of this example and how it relates to the other scenarios shown in the figure.
Lines 581–582: To better demonstrate the model’s temporal transferability, please consider adding tests using images acquired in years other than those used for training or validation. If such experiments are not feasible, it would still be helpful to discuss this limitation and explain how temporal variability may affect model performance.
Technical corrections:
Line 74: Please correct the typographical and punctuation errors in the following sentence: “Similarly, the method proposed by Jiang et al. (2025) also showed severe limitations in spatial transferability.”
Figure 1: Please check whether Band 9 should be labeled as “slope.” If so, please revise the figure or caption accordingly to ensure consistency with the input-band description.