Harnessing Satellite Images with Machine Learning: Spreading Technology and Supercharging Sustainabl

Recent advances in satellite technology have led to an unprecedented increase in the quantity and quality of satellite imagery available. This technologic acceleration presents a significant challenge for researchers and policymakers due to the sheer volume and complexity of these emerging data sets. The key to unlocking the potential of this vast repository of satellite data may lie in the use of machine learning, which can efficiently process and analyze these images for a wide range of applications, from environmental monitoring to urban development.

Carlo Broderick's work focuses on using machine learning to unlock the potential of satellite images by democratizing the use of machine learning technology. By lowering the barriers to using machine learning through open source intercultural collaboration and public advocacy, Carlo and his collaborators hope to spread the use of satellite remote sensing to improve policy outcomes and drive sustainable development.

This presentation provides a brief overview of the satellite image and machine learning technology sector, some of the work being done at UCSB to promote this technology, and the promising programs and ventures pushing this work forward.

Carlo Broderick received his Master's Degree in Environmental Data Science from the Bren School of Environmental Science and Management at UCSB and now works for the National Center for Ecological Analysis and Synthesis (NCEAS) in downtown Santa Barbara.