LS-YOLO: A Novel Model for Detecting Multiscale Landslides With Remote Sensing Images
Why this work is in the frame
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Bibliographic record
Abstract
The landslide is a widespread and devastating natural disaster, posing serious threats to human life, security, and natural assets. Investigating efficient methods for accurate landslide detection with remote sensing images has important academic and practical implications. In this article, we proposed an LS-YOLO, a novel and effective model for landslide detection with remote sensing images. We first built a multi-scale landslide dataset (MSLD) and introduced random seeds in the data augmentation to increase data robustness. Considering the multi-scale characteristic of landslides in remote sensing images, a multi-scale feature extraction module is designed based on Efficient Channel Attention, Average Pooling, and Spatial Separable Convolution. To increase the receptive field of the model, dilated convolution is employed to the decoupled head. Specifically, the context enhancement module consisting of dilation convolutions is added to the decoupled head regression task branch, and then the improved decoupled head is to replace the coupled head in YOLOv5s. Extensive experiments show that our proposed model has high performance for multi-scale landslide detection, and outperforms other object detection models (Faster RCNN, SSD, EfficientDet-D0, YOLOv5s, YOLOv7, and YOLOX). Compared to the baseline model YOLOv5s, the AP of the LS-YOLO for detecting landslides has increased by 2.18% to 97.06%. The code and MSLD will be available at https://github.com/wenjieo/LS-YOLO.
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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 | 0.001 |
| 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 it