Mapping for Climate Change with Deep Learning on Remotely Sensed Imagery


Climate change is one of humanity’s most pressing challenges. Many crucial climate change-related problems can be tackled by developing new technologies to map and monitor the Earth. Due to recent advancements in machine learning together with the increasing resolution and availability of remotely sensed imagery, there is an unprecedented opportunity to develop such mapping technologies. In this session, I describe many existing and emerging approaches for machine learning-based Earth mapping, from classifying the drivers of forest loss to identifying the locations of energy infrastructure and greenhouse gas emission sources. I conclude by outlining the open challenges for developing effective machine learning-based mapping solutions for climate change. This talk is targeted at data scientists in both industry and academia who are interested in learning about how machine learning can be used to help combat and adapt to climate change, and how to use their skills to get involved.


Jeremy is a PhD candidate at Stanford University advised by Professor Andrew Ng. Jeremy is interested in developing machine learning tools for climate change and medicine. His current research is focused on developing machine learning approaches using remote sensing data for mapping energy and transportation infrastructure, with an emphasis on identifying sources of methane emissions globally.

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