25 citations as recorded by crossref.

1. Quantification of Microtopography in Natural Ecosystems Using Close-Range Remote Sensing T. Shukla et al. 10.3390/rs15092387
2. Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images L. Huang et al. 10.1016/j.rse.2019.111534
3. A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes T. Rettelbach et al. 10.3390/rs13163098
4. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
5. Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images W. Zhang et al. 10.3390/rs12071085
6. InSAR estimates of excess ground ice concentrations near the permafrost table S. Zwieback et al. 10.1016/j.isprsjprs.2025.03.004
7. Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongolia – F. Monna et al. 10.1016/j.culher.2020.01.002
8. An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery C. Witharana et al. 10.3390/rs13040558
9. Geometry of last glacial sorted nets from high-resolution airborne data T. Uxa et al. 10.1016/j.geomorph.2023.108615
10. High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska C. Abolt & M. Young 10.1038/s41597-020-0423-9
11. PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic K. Heidler et al. 10.1109/TGRS.2024.3448294
12. Rapid transformation of tundra ecosystems from ice-wedge degradation M. Jorgenson et al. 10.1016/j.gloplacha.2022.103921
13. Heterogeneity in ice-wedge permafrost degradation revealed across spatial scales K. Braun & C. Andresen 10.1016/j.rse.2024.114299
14. Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models C. Abolt et al. 10.5194/tc-13-237-2019
15. New insights into the drainage of inundated ice-wedge polygons using fundamental hydrologic principles D. Harp et al. 10.5194/tc-15-4005-2021
16. Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps I. Nitze et al. 10.3390/rs13214294
17. Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery M. Bhuiyan et al. 10.3390/jimaging6090097
18. Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts A. Clark et al. 10.3390/rs14132982
19. Large-scale mapping of solifluction terraces in the southeastern Tibetan Plateau using high-resolution satellite images and deep learning R. Huang et al. 10.1016/j.geomorph.2023.108626
20. The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada Q. Chang et al. 10.3390/rs17071245
21. Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection C. Witharana et al. 10.1016/j.isprsjprs.2020.10.010
22. Fully automated snow depth measurements from time-lapse images applying a convolutional neural network M. Kopp et al. 10.1016/j.scitotenv.2019.134213
23. Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic L. Huang et al. 10.3390/rs14122747
24. Squeezing Data from a Rock: Machine Learning for Martian Science T. Nagle-McNaughton et al. 10.3390/geosciences12060248
25. Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: part II (Results and Discussion) T. Mathanlal et al. 10.1017/S1473550420000385