15 citations as recorded by crossref.

1. Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. 10.1038/s41597-025-04672-y
2. A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
3. Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane L. Smith et al. 10.1016/j.oregeorev.2022.105119
4. Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. 10.3390/rs15051359
5. Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. 10.1016/j.cageo.2025.105857
6. Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al. 10.1017/aog.2023.67
7. Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al. 10.1126/sciadv.abj8138
8. UAVs for Science in Antarctica P. Pina & G. Vieira 10.3390/rs14071610
9. Paths forward in radioglaciology D. Schroeder 10.1017/aog.2023.3
10. Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al. 10.1017/jog.2021.122
11. Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
12. Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
13. A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. 10.3390/rs15112708
14. Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. 10.1109/TGRS.2023.3303231
15. Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. 10.5194/tc-19-645-2025