Kit et al. (2012)Kit, O., Lüdeke, M., & Reckien, D. (2012). Texture-based identification of urban slums in Hyderabad, India using remote sensing data. Applied Geography (Sevenoaks, England), 32(2), 660-667. http://dx.doi.org/10.1016/j.apgeog.2011.07.016. http://dx.doi.org/10.1016/j.apgeog.2011....
|
Lacunarity |
— |
Hyderabad, India |
— |
— |
— |
— |
— |
|
Owen and Wong (2013)Owen, K., & Wong, D. (2013). An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics. Applied Geography, 38, 107-118. http://dx.doi.org/10.1016/j.apgeog.2012.11.016. http://dx.doi.org/10.1016/j.apgeog.2012....
|
Informality indicators |
DFA + CART |
Guatemala |
118 |
— |
30m |
Accuracy |
87.5% |
|
Ribeiro (2015)Ribeiro, B. (2015). Mapping informal settlements using WorldView- 2 imagery and C4.5 decision tree classifier. In Joint Urban Remote Sensing Event (pp. 1-4). Crete: JURSE.
|
GEOBIA |
C4.5 |
Embu-SP (Brazil) |
225 |
— |
0.5m and 2.0m |
Accuracy and Kappa |
90.94% and 88.98% |
|
Kuffer et al. (2016)Kuffer, M., Pfeffer, K., Sliuzas, R., & Baud, I. (2016). Extraction of slum areas from VHR imagery using GLCM variance. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1830-1840. http://dx.doi.org/10.1109/JSTARS.2016.2538563. http://dx.doi.org/10.1109/JSTARS.2016.25...
|
GLCM and NDVI |
Random Forest |
Mumbai, Ahmedabad and Kigali |
— |
— |
From 0.5m to 2.4m |
Accuracy and Kappa |
Accuracy: 90%; Kappa: 87% |
|
Persello and Stein (2017)Persello, C., & Stein, A. (2017). Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images. IEEE Geoscience and Remote Sensing Letters, 14(12), 2325-2329. http://dx.doi.org/10.1109/LGRS.2017.2763738. http://dx.doi.org/10.1109/LGRS.2017.2763...
|
Automatic convolutional features |
Deep convolutional neural networks |
Dar es Salaam |
— |
125px x 125px |
0.6m |
Average Accuracy |
81.74% |
|
Mboga et al. (2017)Mboga, N., Persello, C., Bergado, J., & Stein, A. (2017). Detection of informal settlements from VHR satellite images using convolutional neurais networks. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5169-5172). New York: IEEE.
|
Automatic Extraction |
CNN+MLP |
Dar es Salaam (Tanzania) |
3060 |
160px x 160px |
0.60m |
Accuracy |
90% |
|
Gadiraju et al. (2018)Gadiraju, K., Vatsavai, R., Kaza, N., Wibbels, E., & Krishna, A. (2018). Machine learning approaches for slum detection using very high resolution satellite images. In IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1397-1404). New York: IEEE. http://dx.doi.org/10.1109/ICDMW.2018.00198. http://dx.doi.org/10.1109/ICDMW.2018.001...
|
Haralick Texture Features, NDBI (Normalized Difference Built-Up Index), Edge Density and Pansharpened Bands |
CNN, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi Layer Perceptron, Gradient Boosting, Random Forest and Adaboost Classifier |
Bengaluru, India |
— |
40px x 40px |
0.5m and 2.0m |
Accuracy, Precision, Recall and F-Measure |
100%, 86%, 72% |
|
|
Ibrahim et al. (2019)Ibrahim, R., Titheridge, H., Cheng, T., & Haworth, J. (2019). predictSLUMS: a new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning. Computers, Environment and Urban Systems, 76, 31-56. http://dx.doi.org/10.1016/j.compenvurbsys.2019.03.005. http://dx.doi.org/10.1016/j.compenvurbsy...
|
incident points |
MLP ANN |
4 cities of Egypt and 1 city of India |
— |
— |
— |
Accuracy |
98% |
|
Proposed leave-one-city-out |
LBP, color histograms, lacunarity |
SVM |
6 Brazilian cities: Arapiraca, Campina Grande, Caruaru, Feira de Santana, Juazeiro do Norte e Mossoró |
3406 |
40px x 40px |
10m |
Accuracy, recall, precision, F1-score |
87.39%, 88.79%, 86.73%, 85.73% |
|
Proposed 10-fold-cross-validation |
LBP, color histograms, lacunarity |
SVM |
6 Brazilian cities: Arapiraca, Campina Grande, Caruaru, Feira de Santana, Juazeiro do Norte e Mossoró |
3406 |
40px x 40px |
10m |
Accuracy, recall, precision, F1-score |
87.22%, 91.81%, 97.95%, 91.81% |
|