Tracking Climate Change With Satellites and Artificial Intelligence (2022-2023)

Climate change is beginning to impact infrastructure, transportation, energy, food and water supplies and human health across the globe, with Africa and Asia being two of the most vulnerable regions to the impacts of global warming. However, these impacts are often difficult to quantify, especially in parts of the Global South where ground-based economic surveys occur infrequently. In regions with incomplete information and knowledge gaps in the strategies needed to adapt to the impacts of climate change, some of the most vulnerable cities incur increased risk of disastrous impacts. 

Informing climate change mitigation and adaptation strategies involves a wide-ranging quantity of data. This requires information on energy infrastructure and access, population, income, demand growth trends, agriculture and land use as well as on vulnerable infrastructure such as transmission lines, water systems and food supply chains. To make the best possible plans and track policy progress and impacts, these data need to be regularly monitored.

This project team worked to democratize access to climate change data and the strategies to acquire those data. Team members developed a self-supervised learning model called GeoNet, consisting of a dataset containing 10 million satellite images — the first and largest ever to capture geospatial, temporal and semantic diversity for remote sensing data. The team tested self-supervised pretraining on GeoNet with five benchmark datasets and found that it outperformed competing methods for three out of the five benchmark datasets.


Fall 2022 – Spring 2023 

Team Outputs

How AI Can Help Fight Climate Change (2023 Fortin Foundation Bass Connections Virtual Showcase)

Tracking Climate Change Using Satellites and Artificial Intelligence (poster by Shufan Xia, Frankie Chiappetta, Margaret Brooks, Alex Desbans, Neel Gajjar, Jules Kourelakos, Saad Lahrichi, Vaishvi Patel, Ada Zhang, Edna Zhang, Kyle Bradbury and Jordan Malof, presented at Fortin Foundation Bass Connections Showcase, Duke University, April 19, 2023)

Artificial intelligence model containing global data on climate change

Team website 

This Team in the News

Students Help Power Up New Ways to Analyze Energy Data

See related teams, Tracking Climate Change with Satellites and Artificial Intelligence (2023-2024) and Creating Artificial Worlds with AI to Improve Energy Access Data (2021-2022), and Data+ summer project, Tracking Climate Change Causes and Impacts With Satellites and AI (2022).


Image: Central Africa Appears to Be Completely On Fire, by NASA Goddard Space Flight Center, licensed under CC BY 2.0

Satellite view of Central African Republic.

Team Leaders

  • Kyle Bradbury, Pratt School of Engineering-Electrical & Computer Engineering|Energy Initiative
  • Jordan Malof, Computer Science Department, University of Montana

/graduate Team Members

  • Francesca Chiappetta, Master of Environmental Management
  • Shufan Xia, Interdisciplinary Data Science - Masters

/undergraduate Team Members

  • Margaret Brooks, Computer Science (BS)
  • Alex Desbans, Electrical & Computer Egr(BSE)
  • Neel Gajjar, Computer Science (BS)
  • Julia Kourelakos, Computer Science (BS)
  • Saad Lahrichi, DKU Interdisciplinary Studies (BS)
  • Vaishvi Patel, Electrical & Computer Egr(BSE)
  • Ruixin Zhang, Cinematic Arts (MIN)
  • Ruohan Zhang, Computer Science (BS)

/yfaculty/staff Team Members

  • Leslie Collins, Pratt School of Engineering-Electrical & Computer Engineering
  • T. Robert Fetter, Nicholas Institute for Environmental Policy Solutions
  • Marc Jeuland, Sanford School of Public Policy
  • Luana Lima, Nicholas School of the Environment-Environmental Sciences and Policy
  • Robyn Meeks, Sanford School of Public Policy