Semantic Segmentation of Satellite Images using Deep Learning

A stark increase in the amount of satellite imagery available in recent years has made the interpretation of this data a challenging problem at scale. Deriving useful insights from such images requires a rich understanding of the information present in them. This thesis explores the above problem by designing an automated framework for extracting semantic maps of roads and highways to track urban growth of cities in satellite images. Devising it as a supervised machine learning problem, a deep neural network is designed, implemented and experimentally evaluated. Publicly available datasets and frameworks are used for this purpose. The resulting pipeline includes image pre-processing algorithms that allows it to cope with input images of varying quality, resolution and channels. Additionally, we review a computational graph approach to building a neural network using the TensorFlow framework