Mapping Slums from Satellite Imagery Using Deep Learning

The Sustainable Development Goals of the UN include inclusive, safe, resilient, and sustainable human settlements. However, many people still live in substandard conditions in urban slums. To improve their conditions, the first step is to map the slum locations. One solution approach is by using Machine Learning models on Remote Sensing imagery. This study examines a Deep Learning model, the U-Net model, for slum delineation in five cities in Maharashtra, India. Our goal is to categorise localities in the five cities as slums or non-slums. Sentinel-2 satellite imagery (having a spatial resolution of 10m) is used as input for the year 2018, utilising bands 2, 3, 4, and 8. QGIS's digitization and visual interpretation tools are used to generate the labels. The model is trained on one city and tested on others, with an accuracy of 95%- 99%. Furthermore, we investigate the role of model parameters like the patch size.