Mapping Complex Landslide Scars Using Deep Learning and High-Resolution Topographic Derivatives from LiDAR Data in Quebec, Canada
Bibliographic record
Abstract
This study evaluates deep learning (DL) models, particularly ResU-Net with attention mechanisms, for mapping landslides in Quebec, Canada, utilizing high-resolution digital elevation model (HRDEM) data and its seven derivatives (slope, aspect, hillshade, curvature, ruggedness, surface area ratio, and max difference from mean). Three scenarios were considered to assess the effectiveness of various features in landslide segmentation: training the model on all features, each feature individually, and on slope and hillshade. Model performance on individual features was significantly poor, while the model trained with hillshade and slope outperformed the model using all seven features, particularly in F1-score (improved by 8% for rotational landslides and 11% for retrogressive landslides) during validation. Furthermore, for the test dataset, model performance on all seven features was compared against slope and hillshade. As a result, for rotational landslides, slope and hillshade achieved F1-scores of 0.68 and 0.93 for rotational and retrogressive landslides, respectively, while the same metrics using all features were 0.61 and 0.83, respectively. This suggests hillshade and slope provide the most relevant information and reduce computational complexity. Overall, the findings enhance our understanding of HRDEM derivatives and emphasize the importance of feature selection in optimizing model performance and reducing computational complexity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".