Landslide Susceptibility Mapping Considering Landslide Local-Global Features Based on CNN and Transformer
Bibliographic record
Abstract
Landslide susceptibility mapping (LSM) is a crucial step in quantitatively assessing landslide risk, essential for geologic hazards prevention. With the rapid development of deep learning models, convolutional neural networks (CNN) and Transformer architectures have been applied to LSM. However, these models still face the challenges of suboptimal mapping accuracy and limited capacity for multi-level landslide features extraction. In this study, we present a CNN-Transformer Local-Global Feature Extraction Network (CTLGNet) that combines the strengths of both CNN and Transformer models to effectively extract both landslide local and global features. We apply this model to LSM in two regions: the Three Gorges Reservoir area and Jiuzhaigou. To begin, nine landslide conditioning factors are selected and analyzed to construct the landslide dataset for LSM. Subsequently, the dataset is randomly split into training, validation, and test datasets in a 6:2:2 ratio to attain LSM results. Then, CTLGNet is compared to CNN, residual neural network (ResNet), densely connected convolutional network (DenseNet), Vision Transformer (ViT), and Fractional Fourier Image Transformer (FrIT) using various evaluation metrics. The results demonstrate that CTLGNet exhibits exceptional landslide prediction and generalization capabilities, outperforming the other five models across all evaluation metrics except Recall, with AUC values of 0.9817 and 0.9693 for the two regions respectively. The LSM results indicate that CTLGNet can effectively extract both landslide local and global features to achieve landslide localization and detail capture. Overall, our proposed framework excels in extracting multi-level landslide features and holds great potential for widespread application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".