Summary of Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy, By Konstantin A. Maslov and Claudio Persello and Thomas Schellenberger and Alfred Stein
Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy
by Konstantin A. Maslov, Claudio Persello, Thomas Schellenberger, Alfred Stein
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Glacier-VisionTransformer-U-Net (GlaViTU) model, a convolutional-transformer deep learning approach, addresses the challenge of accurate global glacier mapping using open satellite imagery. Five strategies are developed for multitemporal global-scale glacier mapping, demonstrating promising results in terms of spatial, temporal, and cross-sensor generalization. The best strategy achieves an intersection over union of greater than 0.85 on previously unobserved images in most cases, with accuracy increasing to 0.90 for regions dominated by clean ice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to map glaciers around the world is developed using computer vision and machine learning. This method uses satellite pictures taken at different times to create an accurate picture of where glaciers are and how they’re changing. The results show that this approach can be very good at predicting where glaciers are, especially in areas with clean ice. Adding special radar data makes it even better. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Machine learning * Transformer
