From Crops to Glaciers: A Deep Learning Framework for Earth Observation

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The goal of this project is to satisfy this need and create a deep learning framework in the form of a neural network that excels at working with satellite imagery. This can be used downstream by earth observation practitioners working with satellite images, for a host of disparate tasks. We will avoid the need for expensive manual annotation of satellite images by employing self-supervised learning [3], a paradigm where we can create pretext tasks to train networks to produce useful features. For everyday photos these pretext tasks include predicting rotations [4], and solving jigsaw puzzles [5]. This project will involve the careful design of pretext tasks suitable for satellite images (this could be e.g. a mixture of temporal or spatial infilling) as well as identifying the most salient data for training these networks. We will also use neural architecture search algorithms [6, 7] so that the underlying network architecture is optimised for use with these images, rather than everyday photos. To demonstrate the effectiveness of our framework we will apply our network downstream to tackle two important environmental problems, employing data from the PlanetScope and Sentinel-1/2 satellites. The problems we will consider are very different in nature; this is an important demonstrator for the robustness of our approach.