12 citations as recorded by crossref.

1. Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al. 10.3390/rs14061352
2. Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools B. Belcher et al. 10.3389/fmars.2023.1157370
3. Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al. 10.5194/tc-16-4273-2022
4. AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
5. Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery N. Gourmelon et al. 10.5194/essd-14-4287-2022
6. AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al. 10.5194/tc-17-3485-2023
7. 12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed M. Moctezuma-Flores et al. 10.1109/JSTARS.2022.3205560
8. Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks R. Lin et al. 10.1002/gj.4615
9. Glacier Retreating Analysis on the Southeastern Tibetan Plateau via Multisource Remote Sensing Data Y. Xiao et al. 10.1109/JSTARS.2023.3243771
10. A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
11. An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers D. Thomas et al. 10.3389/frsen.2023.1161530
12. Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers Y. Lu et al. 10.3390/rs13132595