Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Abstract
:1. Introduction
2. Overview of the Study Area
3. Workflow
3.1. Overall Methodology
- Designing two different training data sets, a) spectral information only, and b) a data set containing both spectral information and topographic factors.
- Applying ANN, SVM and RF methods for landslide detection based on both training data sets and validating the performance for the study area.
- Generating CNN-based patches by considering multiple window sizes from small to large ones.
- Develo** a data augmentation approach for increasing the number of training data sets used for CNNs.
- Structuring CNNs with different layer depths in regard to the range of input window size CNN patches to determine the most efficient CNN setting.
- Testing and validating the performances of each method by using multiple parameters.
3.2. Data
3.3. Random Forest (RF)
3.4. Support Vector Machines (SVM)
3.5. Artificial Neural Network (ANN)
3.6. Convolution Neural Network (CNN)
3.6.1. Multiple Input Window Size CNNs
3.6.2. Augmentation of the Training Data Set
3.6.3. Different Layer Depth CNNs
4. Results
5. Accuracy Assessment
5.1. Quantitative Methods
5.2. Mean Intersection-over-Union (mIOU)
6. Discussion
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Prepared by | # Landslides | Shape | Coverage | Study Reference |
---|---|---|---|---|
USGS | 24915 | Polygon | Gorkha, Nepal | [44] |
IMHE, CAS | 2645 | Polygon | Gorkha, Nepal | [45] |
Gnyawali and Adhikari 2016 | 19332 | Point | Gorkha, Nepal | [46] |
Method | Count | Minimum (ha) | Maximum (ha) | Sum (ha) | Mean (ha) | Standard Deviation (ha) |
---|---|---|---|---|---|---|
247 | 0.175 | 77.16 | 555,63 | 1.54 | 5.37 | |
308 | 0.2 | 54.65 | 530,63 | 1.99 | 6.51 | |
281 | 0.175 | 96.32 | 480,52 | 1.15 | 4.97 | |
321 | 0.18 | 157.64 | 447,28 | 1.65 | 7.08 | |
286 | 0.175 | 136.85 | 524,32 | 1.89 | 9.75 | |
341 | 0.185 | 170.05 | 426,05 | 0.96 | 4.59 | |
306 | 0.21 | 208.48 | 784,79 | 1.93 | 11.08 | |
335 | 0.205 | 174.4 | 508,31 | 2.16 | 6.43 | |
314 | 0.2 | 204.8 | 426,83 | 2.37 | 11.23 | |
385 | 0.22 | 154.72 | 478,02 | 1.1 | 3.46 | |
D- | 268 | 0.195 | 58.87 | 467,52 | 1.27 | 4.28 |
D- | 277 | 0.2 | 76 | 509,83 | 1.84 | 6.45 |
D- | 306 | 0.22 | 65.6 | 589,93 | 1.49 | 4.39 |
D- | 319 | 0.22 | 74.08 | 505,43 | 2.3 | 6.59 |
421 | 0.175 | 322.16 | 754,85 | 2.24 | 14.31 | |
514 | 0.175 | 352.09 | 798,72 | 1.69 | 15.77 | |
333 | 0.175 | 117.9 | 565,93 | 1.47 | 7.29 | |
459 | 0.18 | 282.02 | 568,11 | 1.27 | 11.62 | |
ANN5 | 489 | 0.175 | 117.95 | 991,98 | 1.05 | 4.64 |
ANN8 | 546 | 0.175 | 153.95 | 1125,75 | 0.99 | 4.5 |
Method | TP (ha) | FP (ha) | FN (ha) | Precision (%) | Recall (%) | F1 (%) | mIOU (%) |
---|---|---|---|---|---|---|---|
368 | 113.23 | 74.4 | 76.47 | 83.18 | 79.68 | 66.23 | |
344.07 | 146.64 | 39.92 | 70.11 | 89.6 | 78.67 | 64.84 | |
397.8 | 59.63 | 23.09 | 83.31 | 92.8 | 87.8 | 78.26 | |
345.27 | 63.89 | 38.12 | 79.33 | 86.54 | 82.78 | 70.62 | |
351.02 | 145.89 | 27.41 | 70.51 | 92.85 | 79.94 | 66.8 | |
260.45 | 87.98 | 77.62 | 74.74 | 77.03 | 75.87 | 61.13 | |
325.24 | 380.03 | 79.52 | 53.88 | 82.69 | 65.25 | 48.42 | |
279.58 | 182.97 | 45.76 | 60.44 | 85.93 | 70.96 | 55 | |
210 | 110.27 | 106.56 | 66.02 | 67.95 | 66.97 | 50.35 | |
226.61 | 156.32 | 95.09 | 59.12 | 66.33 | 70.28 | 47.29 | |
D- | 297.63 | 124.53 | 45.36 | 70.5 | 86.77 | 77.79 | 63.66 |
D- | 301.08 | 162.31 | 46.44 | 64.97 | 86.63 | 74.25 | 59.05 |
D- | 298.55 | 201.36 | 90.02 | 59.72 | 76.83 | 67.2 | 50.6 |
D- | 273.14 | 194.69 | 37.6 | 58.38 | 87.9 | 70.56 | 54.04 |
385.9 | 318.28 | 50.67 | 54.8 | 88.39 | 67.65 | 51.12 | |
403.07 | 395.65 | 58. 47 | 50.51 | 87.38 | 64.01 | 47.07 | |
393.9 | 86.71 | 85.32 | 81.95 | 82.19 | 82.07 | 69.6 | |
380.2 | 89.6 | 98.31 | 80.9 | 79.45 | 80.17 | 66.9 | |
ANN5 | 499,83 | 152,03 | 340,12 | 76,7 | 59, 53 | 67,03 | 50,41 |
ANN8 | 445,9 | 459,81 | 220,04 | 49,22 | 66,95 | 56,73 | 39,6 |
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Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Meena, S.R.; Tiede, D.; Aryal, J. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens. 2019, 11, 196. https://doi.org/10.3390/rs11020196
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing. 2019; 11(2):196. https://doi.org/10.3390/rs11020196
Chicago/Turabian StyleGhorbanzadeh, Omid, Thomas Blaschke, Khalil Gholamnia, Sansar Raj Meena, Dirk Tiede, and Jagannath Aryal. 2019. "Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection" Remote Sensing 11, no. 2: 196. https://doi.org/10.3390/rs11020196