Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia
Abstract
:1. Introduction
1.1. Background
1.2. Conceptual Framework
1.3. Study Area
2. Methodology
2.1. Material
2.2. Bandung Slum Characteristics and Image Features
2.3. Feature Selection
2.4. Classification
2.5. Evaluation of Machine Learning Slum Mapping
2.6. Experimental Setup
3. Results
3.1. GLCM and LBP Assessment
3.2. Sequential Feature Selection
3.3. Support Vector Machine and Random Forest
3.4. Classified Slum Map
3.5. Extending the Approach to a Larger Area
3.6. Comparing the Classified Map with the Survey-Based Slum Mapping Map
3.7. Strengths and Weaknesses
4. Discussion
4.1. Quantitative Analysis
4.2. Qualitative Analysis
4.2.1. Classified Map
4.2.2. Strengths and Weaknesses
4.2.3. Perception of the Stakeholders
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
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Data | Year | Data Sources | Category |
---|---|---|---|
Pleiades (pansharpened) images. Res: 0.5 m | 2016 (July and August) | European Space Agency (ESA) | Primary |
Slum boundaries | 2015 | Ministry of Public Works and Housing | Secondary |
Administrative boundary of Bandung city | 2015 | Municipality of Bandung | Secondary |
Historical Google Earth images | 2013–2016 | Online data | Secondary |
Validated slum boundaries | October 2017 | Ground truth checking | Primary |
Expert interview scripts | October 2017 | Interview | Primary |
GSO Dimension | Indicator | Local Indicator | Image Feature |
---|---|---|---|
Environs | Location | Hazardous areas, in between small alleys | No image feature was used explain the environs level |
Neighborhood Characteristics | Proximity to industrial, commercial, formal residential, bus stations, and smelly and dirty areas | ||
Settlement | Shape | Irregular pattern, elongated formation following the river or railway | Contextual (PanTex, LBP, GLCM) and morphological features (APPR) |
Density | High density (more than 250 unit/ha), high roof coverage, less vegetation | Contextual (PanTex, LBP, GLCM) and spectral features (NDVI) | |
Object | Access Network | Unpaved or poorly constructed streets, width ≤2.5 m, covered conduits or without conduits | Contextual features (PanTex, LBP, GLCM) |
Building Characteristics | Permanent and nonpermanent structures, with the roofs made from corrugated iron, asbestos, plastic, fiber, and clay tiles; building size from 10–60 m2; poor sanitation, using well water or bought water | Spectral (original band) and morphological features |
Features | Number of Bands |
---|---|
Original band | 4 |
NDVI | 1 |
PanTex with contrast adjustment 13 | 1 |
PanTex with contrast adjustment 27 | 1 |
PanTex with contrast adjustment 53 | 1 |
PanTex with contrast adjustment 105 | 1 |
GLCM 105 | 32 |
LBP | 19 |
APPR | 18 |
Kelurahan | Area | Training and Validation Set | Training and Validation Pixel Number | Testing Set | Testing Pixel Number |
---|---|---|---|---|---|
Antapani | 1003 × 1004 | 28 polygons | 349 | 69 polygons | 866 |
Babakan | 1002 × 1002 | 31 polygons | 385 | 50 polygons | 635 |
Campaka 1 | 1004 ×1004 | 32 polygons | 400 | 63 polygons | 790 |
Campaka2 | 1002 × 1002 | 30 polygons | 374 | 49 polygons | 608 |
Cigondewah | 1002 × 1004 | 36 polygons | 455 | 69 polygons | 866 |
Pasir Impun-1 | 1005 × 1006 | 36 polygons | 453 | 41 polygons | 519 |
Pasir Impun-2 | 1003 × 1003 | 29 polygons | 350 | 44 polygons | 557 |
Sekejati | 1002 × 1007 | 35 polygons | 434 | 61 polygons | 753 |
Tamansari 1 | 1002 × 1009 | 32 polygons | 398 | 54 polygons | 679 |
Tamansari 2 | 1002 × 1001 | 36 polygons | 450 | 59 polygons | 741 |
Number of pixels in the training and validation sets: 4048; Number of pixels of all training sets (80%): 3238; and of all validation sets (20%): 810 | Number of all testing sets: 7014 pixels |
GLCM 13 | GLCM 27 | GLCM 53 | GLCM 105 |
---|---|---|---|
72.7% | 77.7% | 82.1% | 83.8% |
81.3% | 81.1% | 81.2% | 81.6% | 80.7% |
No. | Features | No. | Features | No. | Features |
---|---|---|---|---|---|
1 | PanTex window size 105 | 12 | GLCM Dissimilarity Band-1 | 23 | GLCM Entropy Band-3 |
2 | PanTex window size 53 | 13 | LBP | 24 | GLCM Entropy Band-2 |
3 | LBP | 14 | LBP | 25 | APPR area 200 opening |
4 | PanTex window size 27 | 15 | GLCM Entropy Band-1 | 26 | LBP |
5 | LBP | 16 | GLCM Dissimilarity Band-2 | 27 | GLCM Correlation Band-2 |
6 | LBP | 17 | GLCM Variance Band-1 | 28 | GLCM Mean Band-1 |
7 | GLCM Homogeneity Band-1 | 18 | GLCM Variance Band-2 | 29 | Green Band |
8 | GLCM Homogeneity Band-2 | 19 | GLCM Dissimilarity Band-3 | 30 | GLCM Second Moment Band-1 |
9 | GLCM Homogeneity Band-3 | 20 | GLCM Variance Band-4 | 31 | LBP |
10 | PanTex window size 13 | 21 | GLCM Correlation Band-1 | 32 | GLCM Correlation Band-3 |
11 | GLCM Correlation Band-4 | 22 | GLCM Variance Band-3 |
No. | Feature Type | Mean Decrease (Gini) | No | Feature Type | Mean Decrease (Gini) |
---|---|---|---|---|---|
1 | PANTEX 53 | 57.998 | 18 | GLCM—Variance band 1 | 25.043 |
2 | GLCM—Correlation band 4 | 52.099 | 19 | GLCM—Dissimilarity band 1 | 24.272 |
3 | PANTEX 105 | 44.918 | 20 | GLCM—Variance band 3 | 24.246 |
4 | PANTEX 27 | 42.463 | 21 | GLCM—Variance band 2 | 24.066 |
5 | PANTEX 13 | 36.494 | 22 | LBP | 23.032 |
6 | GLCM—Homogeneity band 1 | 32.559 | 23 | GLCM—Homogeneity band 3 | 22.692 |
7 | LBP | 30.548 | 24 | GLCM—Homogeneity band 4 | 22.519 |
8 | GLCM—Correlation band 1 | 30.173 | 25 | GLCM—Mean band 1 | 21.647 |
9 | GLCM—Second moment band 3 | 29.326 | 26 | NDVI | 21.6 |
10 | GLCM—Homogeneity band 2 | 29.014 | 27 | LBP | 21.351 |
11 | LBP | 27.781 | 28 | LBP | 21.181 |
12 | GLCM—Second moment band 2 | 26.959 | 29 | LBP | 20.939 |
13 | GLCM—Variance band 4 | 26.866 | 30 | GLCM—Second moment band 4 | 20.866 |
14 | GLCM—Correlation band 2 | 26.374 | 31 | GLCM—Contrast band 3 | 20.676 |
15 | GLCM—Second moment band 1 | 26.148 | 32 | LBP | 20.633 |
16 | GLCM—Correlation band 3 | 26.137 | 33 | GLCM—Entropy band 2 | 20.408 |
17 | LBP | 26.038 |
Without SFS | With SFS | ||
---|---|---|---|
SVM | RF | SVM | RF |
86.5% | 85.2% | 88.5% | 85.2% |
No. | Selected Area | Time (s) | OA | Kappa | Completeness | Correctness | F1 Score |
---|---|---|---|---|---|---|---|
1 | Antapani | 0.028 | 0.859 | 0.709 | 0.938 | 0.831 | 0.881 |
2 | Babakan | 0.023 | 0.938 | 0.861 | 0.876 | 0.941 | 0.907 |
3 | Campaka-1 | 0.021 | 0.882 | 0.758 | 0.861 | 0.941 | 0.899 |
4 | Campaka-2 | 0.019 | 0.799 | 0.599 | 0.721 | 0.8601 | 0.784 |
5 | Cigondewah | 0.022 | 0.869 | 0.730 | 0.804 | 0.878 | 0.839 |
6 | Pasir Impun-1 | 0.020 | 0.720 | 0.033 | 0.176 | 0.228 | 0.199 |
7 | Pasir Impun-2 | 0.020 | 0.863 | 0.704 | 0.815 | 0.807 | 0.811 |
8 | Sekejati | 0.025 | 0.806 | 0.588 | 0.911 | 0.789 | 0.846 |
9 | Tamansari-1 | 0.023 | 0.873 | 0.746 | 0.845 | 0.888 | 0.866 |
10 | Tamansari-2 | 0.021 | 0.869 | 0.738 | 0.880 | 0.859 | 0.869 |
All | 0.294 | 0.856 | 0.712 | 0.845 | 0.849 | 0.847 | |
Training Time | 3.673 |
No. | Selected Area | Time (s) | OA | Kappa | Completeness | Correctness | F1 Score |
---|---|---|---|---|---|---|---|
1 | Antapani | 0.075 | 0.895 | 0.784 | 0.956 | 0.868 | 0.91 |
2 | Babakan | 0.057 | 0.924 | 0.836 | 0.936 | 0.857 | 0.895 |
3 | Campaka-1 | 0.067 | 0.918 | 0.826 | 0.950 | 0.918 | 0.934 |
4 | Campaka-2 | 0.061 | 0.803 | 0.606 | 0.727 | 0.861 | 0.788 |
5 | Cigondewah | 0.072 | 0.908 | 0.813 | 0.935 | 0.856 | 0.894 |
6 | Pasir Impun-1 | 0.054 | 0.726 | 0.127 | 0.294 | 0.3 | 0.297 |
7 | Pasir Impun-2 | 0.053 | 0.856 | 0.698 | 0.875 | 0.761 | 0.815 |
8 | Sekejati | 0.064 | 0.908 | 0.811 | 0.929 | 0.914 | 0.921 |
9 | Tamansari-1 | 0.066 | 0.908 | 0.816 | 0.891 | 0.921 | 0.906 |
10 | Tamansari-2 | 0.057 | 0.903 | 0.806 | 0.939 | 0.871 | 0.904 |
All | 0.510 | 0.885 | 0.769 | 0.894 | 0.865 | 0.879 | |
Training Time | 1.928 |
Actual | ||
---|---|---|
Predicted | Slums | Nonslums |
Slums | 18 | 16 |
Nonslums | 9 | 157 |
Overall Accuracy | Kappa | Completeness | Correctness | F1 Score |
---|---|---|---|---|
87.5% | 0.518 | 0.667 | 0.529 | 0.59 |
Factors | Machine Learning-Based Slum Mapping (MLBSM) | Survey-Based Slum Mapping (SBSM) |
---|---|---|
Cost |
|
|
Human resources |
|
|
Infrastructures |
|
|
Processing Time | Approximately one month depending on the capacity of the computer, as well as surveys on the field to get the training set. | Approximately six months depending on the capacity of surveyors and participatory process with the community. |
Spatial Coverage | With one set of the resources (human, and infrastructures) in 2 months, it possibly produces one city | With one set of the resources (human, and infrastructures) in 2 months, it possibly produces only some parts of the city depending on how large the city is. |
Accuracy | 88.5% of the reference (ground truth data) by the highest accuracy result from SVM | 80% (claimed by ministry); However, it is only an assumption, because they do not have a mechanism for the accuracy assessment. They realized results depend on surveyor’s understanding. Limitations are also caused by time and geographic barriers to collect data on the ground, meaning sometimes the surveyor only estimates the data. |
Degree of automation | 33.33% From the three steps (surveying, making the slum maps, validating), one step (making the slum maps) is automated | 0% |
Maintenance | The parameter should be adjusted for another city according to the local slum characteristics | Not relevant |
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Leonita, G.; Kuffer, M.; Sliuzas, R.; Persello, C. Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia. Remote Sens. 2018, 10, 1522. https://doi.org/10.3390/rs10101522
Leonita G, Kuffer M, Sliuzas R, Persello C. Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia. Remote Sensing. 2018; 10(10):1522. https://doi.org/10.3390/rs10101522
Chicago/Turabian StyleLeonita, Gina, Monika Kuffer, Richard Sliuzas, and Claudio Persello. 2018. "Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia" Remote Sensing 10, no. 10: 1522. https://doi.org/10.3390/rs10101522