Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
"> Figure 1
<p>Overview of the workflow of active learning for landslide detection based on the SVM.</p> "> Figure 2
<p>The study area: the Reserva Biológica San Francisco (RBSF), (<b>a</b>) hillshade with 10 m × 10 m resolution in 1997, and (<b>b</b>) the orthophoto with 0.3 m × 0.3 m resolution in early 2001.</p> "> Figure 3
<p>Performance versus epochs: mean AUROCs of SVMs across all 150 repetitions for different sampling methods in the target area.</p> "> Figure 4
<p>Standard deviation of AUROC estimates across all 150 repetitions for different sampling methods in the target area.</p> "> Figure 5
<p>Optimal hyperparameters for the SVM with margin sampling in epochs 0, 5, 10, and 20 for all 150 repetitions.</p> "> Figure 6
<p>Variable importance plot for SVM using margin sampling in each epoch.</p> "> Figure 7
<p>ALE plots of the most important predictors (green chromatic coordinate, GCC; red chromatic coordinate, RCC; logarithm of the size of the upslope contributing area, log.carea) for the SVM with margin sampling in repetition 1, epoch 20.</p> "> Figure 8
<p>Landslide classification maps of SVM with margin sampling in epochs 0, 5, 10, and 20. Predicted probabilities are classified into four classes (very high, high, moderate, and low) using the top 4th, 10th, and 50th percentile as class boundaries.</p> "> Figure 9
<p>Number of landslide instances in the training set in each epoch using SVM with different sampling strategies.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Active-Learning and Traditional Learning Strategies Used
2.1.1. Uncertainty Sampling
- (1)
- Least Confidence
- (2)
- Margin Sampling (MS)
- (3)
- Entropy measure
2.1.2. Query by Committee
2.1.3. Random Sampling as a Baseline
2.2. Landslide Classification Model
2.3. Repetition and Performance Estimation
2.4. Study Area and Data
3. Results
3.1. Model Performance
3.2. Model Interpretation
4. Discussion
4.1. Potential of SVM with AL
4.2. Limitations of SVM with AL
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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RCC | GCC | Slope | Plancurv | Profcurv | Log.carea | Cslope | |
---|---|---|---|---|---|---|---|
RCC | 100 | −36 | −11 | 9 | 8 | −16 | −17 |
GCC | −36 | 100 | 14 | −18 | −18 | 34 | 28 |
Slope | −11 | 14 | 100 | 3 | 3 | −3 | 75 |
plancurv | 9 | −18 | 1 | 100 | 52 | −68 | −17 |
profcurv | 8 | −18 | 3 | 52 | 100 | −58 | −23 |
log.carea | −16 | 34 | −3 | −68 | −58 | 100 | 22 |
cslope | −17 | 28 | 75 | −17 | −23 | 22 | 100 |
Sampling Strategy | Non-Landslide | Landslide |
---|---|---|
Margin sampling | 1013 | 447 |
Query by committee | 1280 | 180 |
Random sampling | 1415 | 45 |
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Wang, Z.; Brenning, A. Active-Learning Approaches for Landslide Mapping Using Support Vector Machines. Remote Sens. 2021, 13, 2588. https://doi.org/10.3390/rs13132588
Wang Z, Brenning A. Active-Learning Approaches for Landslide Mapping Using Support Vector Machines. Remote Sensing. 2021; 13(13):2588. https://doi.org/10.3390/rs13132588
Chicago/Turabian StyleWang, Zhihao, and Alexander Brenning. 2021. "Active-Learning Approaches for Landslide Mapping Using Support Vector Machines" Remote Sensing 13, no. 13: 2588. https://doi.org/10.3390/rs13132588
APA StyleWang, Z., & Brenning, A. (2021). Active-Learning Approaches for Landslide Mapping Using Support Vector Machines. Remote Sensing, 13(13), 2588. https://doi.org/10.3390/rs13132588