{Detection of Slums from Very High-Resolution Satellite Images Using Machine Learning Algorithms: A Case Study of Fustat Area in Cairo, Egypt}

Citation:
Salem, M., Tsurusaki N., Eissa A., & Osman T. (2020).  {Detection of Slums from Very High-Resolution Satellite Images Using Machine Learning Algorithms: A Case Study of Fustat Area in Cairo, Egypt}. 6th International Exchange and Innovation Conference on Engineering & Sciences. 219–224.

Abstract:

Slums are a global urban challenge, particularly in big cities in most developing countries where they are growing faster than governments control. However, detection of slums is a big challenge for such countries due to fast growing there and difficulty of field survey. To address this challenge, this study uses a novel method to detect slums from very high-resolution (VHR) satellite images using machine learning algorithms and roads network derived from OpenStreetMap. This method has been applied to Fustat Area in the center of Cairo, Egypt where slums highly exist. The result of this study has detected eight slums with areas that ranged from 2.4 ha to 28.3 ha. The accuracy of the result has been verified by the kappa index which showed a high accuracy of 0.93. The results of this study are important for planners and decision makers to help them in developing such areas.

Notes:

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