Interested in using satellites and machine learning to help keep decision-makers better informed about climate change? Interested in learning about cutting-edge computer vision techniques for analyzing satellite imagery and how to scale them up globally? Come join a student team collaborating with the Energy Data Analytics Lab as we democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will help to build a globally scalable foundation model to enable near real-time tracking of climate change causes and impacts. A foundation model is a model (usually a deep neural network) that has been trained on a large and diverse set of data, after which it can be adapted to a variety of different (but related) inference tasks with a small fraction of additional training data and computation. Leveraging recent developments in self-supervised learning, we will develop the dataset and training paradigm for creating this foundation model and begin applying it to real-world data. A model developed using such a dataset will enhance climate change mitigation/adaptation monitoring and planning by developing robust features that can be used to monitor a broad range of climate change contributing factors (e.g. energy infrastructure and use, agricultural activity) and impacts (e.g. economic impacts and human migration) for informing climate mitigation and adaptation strategies.
Project Lead: Kyle Bradbury
Project Manager: Paul Markakis
