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Advancing Global Food Security and SDGs with Machine Learning and Earth Observations
Satellite Earth observation (EO) data is rapidly gaining interest in the AI community due to the massive datasets involved as well as the opportunities for using AI and EO data to address urgent challenges related to climate change, the environment, agriculture and food security, and humanitarian needs. However there are currently many challenges for developing AI systems that use EO data for practical applications, namely, there are limited public labeled datasets, a lack of harmonization across labels and source data, and substantial effort is required to make EO data “ML-ready”. In this talk, Hannah Kerner of the University of Maryland presents approaches to address these challenges to create AI+EO systems that can be integrated into real-world applications for advancing global food security and sustainable development.
Hannah Kerner is currently an assistant research professor at the University of Maryland, College Park. Her research focuses on developing machine learning solutions for remote sensing applications in agricultural monitoring, food security, and Earth/planetary science. She is the machine learning lead and U.S. domestic co-lead for NASA Harvest, NASA’s agriculture and food security initiative that is run out of the University of Maryland