Deploying AI for Climate Adaptation: A Spotlight on Disaster Management


Big data and artificial intelligence have enabled numerous applications for humanitarian and social good. In terms of climate change, machine learning, deep learning, and computer vision approaches have proven to be useful for adaptation and mitigation. In a broad initial overview, we’ll highlight nine major areas in which artificial intelligence is key in the fight against this crisis: electricity systems, transportation, buildings and cities, industry, farms and forests, carbon dioxide removal, climate prediction, societal impacts, solar geoengineering. From harnessing deep learning-based computer vision techniques for infrastructure damage assessment after natural disasters using satellite imagery, to utilizing natural language processing technologies to analyze climate-related legislation, we contend that AI is a necessary tool in years ahead. At the same time, sustainable and responsible use of deep learning models is key. Particularly, the notably large energy consumption of AI systems themselves have come under scrutiny; especially with the recent popularity of deep learning (DL) since approximately 2012, high-level computations have raised the overall energy consumption by 300,000 times or more. Balancing this concern, in addition to other considerations like model interpretability, accessibility, and fairness, are crucial challenges to tackle ahead. In the second half of this talk, we will highlight the specific use case of deploying mobile app-based disaster recovery machine learning models, where computer vision models are trained on real-time satellite imagery and/or social media data. The tools used primarily consist of Python software and the PyTorch library to program convolutional neural networks. This technology aids communities, nongovernmental organizations, and local governments in directing disaster relief aid in a timely, targeted manner.

Background Knowledge
Attendees should know general Python syntax and preferably be familiar with PyTorch, but this familiarity beyond this is not required. The talk is not overly technical.


Thomas Y. Chen is a student researcher in machine learning from New Jersey who is passionate about computer vision, artificial intelligence, and data science applications for Earth science. Recently, he developed an interpretable AI model to detect and assess infrastructure damage from satellite imagery. He is a member of the Research Data Alliance and serves on the U.S. Technology Policy Committee of the Association for Computing Machinery (ACM USTPC).

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