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About Research Collection
This article compares the performance of deep learning and traditional machine learning models for mapping landslides using earth observation data. It introduces a modified U-Net model with ResNet34 blocks for feature extraction and semantic segmentation of landslides. The model was trained on image tiles from Douglas County, Oregon using focal Tversky loss and cross-entropy loss functions. The deep learning method achieved a Matthews correlation coefficient score of 0.495 and probability of detection of 0.72, outperforming conventional pixel-based and object-based machine learning strategies for landslide mapping.
Detailed Information
| Author: | ['dipali1631'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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