Combining spatial response features and machine learning classifiers for landslide susceptibility mapping
Bibliographic record
Abstract
Reliable landslide susceptibility mapping (LSM) is essential for disaster prevention and mitigation. This study develops a deep learning framework that integrates spatial response features and machine learning classifiers (SR-ML). The method has three steps. First, depthwise separable convolution (DSC) extracts spatial features to prevent confusion of multi-factor features. Second, spatial pyramid pooling (SPP) extracts response features to obtain features under different scales. Third, the high-level features are fused into prepared ML classifiers for more effective feature classification. This framework effectively extracts and uses different-dimension features of samples, explores ML classifiers for beneficial feature classification, and breaks through the limitation of fixed input sample sizes. In the Yarlung Zangbo Grand Canyon region, data on 203 landslides and 11 conditioning factors were prepared for availability verification and LSM. The evaluation indicated that the area under the receiver operating characteristic curve (AUC) for the proposed SR and SR-ML achieved 0.920 and 0.910, which were 6.6% and 5.6% higher than the random forest (RF, with the highest AUC in ML group) method, respectively. Furthermore, the framework using 64×64 size inputs had the lowest mean error of 0.01, revealing that samples considering landslide scales could improve performance for LSM.
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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 | 0.001 | 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".