25 Jan 2019
Brief communication | 25 Jan 2019
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
Charles J. Abolt et al.
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12 citations as recorded by crossref.
- 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
- 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
- New insights into the drainage of inundated ice-wedge polygons using fundamental hydrologic principles D. Harp et al. 10.5194/tc-15-4005-2021
- A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes T. Rettelbach et al. 10.3390/rs13163098
- Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps I. Nitze et al. 10.3390/rs13214294
- 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
- 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
- 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
- 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
- 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
- Fully automated snow depth measurements from time-lapse images applying a convolutional neural network M. Kopp et al. 10.1016/j.scitotenv.2019.134213
- 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
Latest update: 06 Jul 2022