Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
Bibliographic record
Abstract
A landslide is one of the most common geological disasters, which is associated with great destructive power and harm. In recent years, semantic segmentation models have been applied to landslide recognition research and have made some achievements. However, the current method still has issues, overlooking small targets like fine cracks, missegmenting boundaries, and struggling to differentiate spectral signatures such as those of different rock types in landslide-prone areas. In this paper, a landslide detection model based on the DeepLabv3+ framework, DeepLabv3+-ResNet101-ECA, is proposed. The backbone feature extraction network of DeepLabv3+ is replaced with ResNet101 to enhance the feature extraction ability of the model for small objects. The ECA attention mechanism is integrated into the model to improve the accuracy of the object segmentation and improve the detection accuracy. Taking the landslide in Bijie City, Guizhou Province, as the research object, compared with the original DeepLabv3+ model, the precision of DeepLabv3+-ResNet101-ECA is increased by 1.17%, the recall rate is increased by 2%, the F1 score is increased by 0.96%, and the MIou is increased by 2.36%. Finally, transfer learning is used to verify the generalization ability of the model. The results show that the improved model has a better detection effect on landslides.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".