The main idea of the paper is that convolutional neural networks can be applied to very highresolution satellite imagery in order to classify New Delhi into formal (planned colony) vs. informal settlements (Jhuggi Jhopri Clusters). We show that very high-resolution satellite imagery along with convolutional neural networks can achieve high classification accuracy of 95.81%. We find that pretrained deep learning models for computer vision trained on standard image datasets can be effective for classification of informal settlements using satellite imagery, even when there is not a significant amount of training data. Deep learning models can learn image features without hand-crafted features and when coupled with the proliferation of cloud-based computer vision services could democratize the analysis of satellite imagery for humanitarian and developmental purposes.
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