This project is also part of Duke’s first Climate+ cohort.
A student team working with the Energy Data Analytics Lab will work to 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 work towards building the first “foundation model” specifically for remote sensing imagery for the purpose of extracting climate change relevant content at scale to enable near real-time tracking of climate 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 additional training data and computation. Leveraging recent developments in self-supervised learning, we will develop the dataset for creating this foundation model and begin training it on real-world data. A model developed using such a dataset will enhance climate change mitigation/adaptation monitoring and planning through 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: Zachary Calhoun
Watch the team’s final presentation below:
See what our students had to say about this 2022 summer project: