Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Conceptualizing Deprivation
2.2. Available Data
2.3. Understanding Slums’ Socio-Economic Variations
2.4. Building Image-Based Models to Predict the DIMD
2.4.1. Sample and Image Preparation
2.4.2. CNN-Based Model to Predict the DIMD
Classification Problem
Transfer Learning: Regression Problem
2.4.3. PCR Models Using Hand-Crafted and GIS Features
- Spectral information (Table 3; Spectral info.).
- Two sets of the most common texture features; grey level co-occurrence matrix (GLCM) and local binary pattern (LBP). We generate GLCM features in four directions and four lags (i.e., 1 to 4 pixels) and based on [17], we calculate three properties—entropy, variance, and contrast—on each feature. We calculate GLCM properties on each band of a patch and consider the mean value as the property value (Table 3; GLCM).
- To include LBP features in the model, we extract only uniform patterns (with a maximum of two transitions), which provide the most important textural information about an image [60]. Based on [18], we calculate (i.e., rotation invariant uniform patterns with a radius of 1, which considers eight neighbors), , and with linear interpolation. We average the extracted LBP of each band to obtain the value for a patch considering the whole patch as a cell (Table 3; LBP).
- GIS features; as road data are not consistent enough to perform network analysis, we calculate the minimum Euclidean distances from each of the public service/land use (Table 3; GIS) to a patch’s center points. Distance to different land uses and public services have been used to calculate the degree of deprivation of settlements especially in UK deprivation indices (e.g., [44]). We consider the town hall as the center of the city, which is very close to the geographic center of the city. Using the elevation layer, we calculate the mean elevation and mean slope within each patch.
2.4.4. Ensemble Regression Models
3. Results
3.1. DIMDs
3.2. CNN-Based Model Performance
3.3. PCR Model Performance
3.4. Ensemble Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data Set | Indicators |
---|---|
HH (16 indicators 118 categories) | Caste, Highest educational level obtained, Dependency rate, Distance to healthcare, Income, Ration Card, Water source quality (summer and other seasons), Toilet facility, Access to electricity, Crowdedness, Dwelling age, Floor material, Wall material, Roof material, Travel time to services |
QS (35 indicators 109 categories) | Dominant building type, Number of floors, Dominant building footprint size, Wall material, Roof material, Dominant shape of building, Overall state of buildings, Overall building appearance, Open spaces/green spaces, Appearance of open space, Presence of roads, Road pavement, Road material, Road width, Cables for electricity, Presence of footpaths, Footpath material, Streetlight, Pollution (smell, noise, waste), Open sewers, Presence of public toilet, Waterbody, Economic activities, Type of economic activities, Dominant land use around the slum, Feeling safe?, Are people interacting?, Are there vehicles visible?, Temple, Clothes of people, Having jewelry?, Hair of children, Children toys |
Hyper-Parameter | Value |
---|---|
Batch size | 64 |
Learning rate | Decreases logarithmically from 0.01 to 0.00001 |
Weight decay | 0.0005 |
Momentum | 0.9 |
Feature Name | Specification | # of Features |
---|---|---|
Spectral info. | Band mean and standard deviation, NDVI mean and standard deviation | 8 + 2 |
GLCM | 4 directions [i 0][i i][0 i][−i i]); i = 1,2,3,4; three properties | 16 + 16 + 16 |
LBP | , , | 10 + 18 + 26 |
GIS | Transportation: distance to (1) main road, (2) bus stop, (3) railway, (4) railway station; Healthcare: distance to (5) healthcare, (6) pharmacy; Other services: distance to (7) school, (8) leisure activities; Centrality: distance to (9) town hall; Environment: (10) distance to waterbody (11) elevation mean (12) slope mean. | 12 |
Linear | Interactions | Quadratic + Interactions | 3rd Degree + Interactions | 4th Degree + Interactions | 5th Degree + Interactions | 6th Degree + Interactions | |
---|---|---|---|---|---|---|---|
CNN + hand-crafted + GIS | 0.73 | 0.73 | 0.73 | 0.75 | 0.74 | 0.74 | 0.41 |
CNN + hand-crafted | 0.67 | 0.67 | 0.65 | 0.38 | 0.62 | −0.23 | <−1.00 |
CNN + GIS | 0.71 | 0.71 | 0.71 | 0.65 | 0.49 | <−1.00 | <−1.00 |
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Share and Cite
Ajami, A.; Kuffer, M.; Persello, C.; Pfeffer, K. Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks. Remote Sens. 2019, 11, 1282. https://doi.org/10.3390/rs11111282
Ajami A, Kuffer M, Persello C, Pfeffer K. Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks. Remote Sensing. 2019; 11(11):1282. https://doi.org/10.3390/rs11111282
Chicago/Turabian StyleAjami, Alireza, Monika Kuffer, Claudio Persello, and Karin Pfeffer. 2019. "Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks" Remote Sensing 11, no. 11: 1282. https://doi.org/10.3390/rs11111282