Satellite data offers exciting new opportunities to identify and address global and regional sustainability challenges, providing unprecedented volumes of data on natural environments and human activities with broad spatial coverage. However, this data is useless if meaningful information cannot be extracted and acted upon, a significant challenge given the sheer size of these datasets. Emerging data science and statistical approaches (such as machine learning) provide great potential for analyzing and generating scientific and policy insights from the underexplored universe of satellite data.

This session broadly invites research that uses machine learning on satellite data to tackle sustainability challenges, including but not limited to: climate change; air pollution; extreme events; and energy poverty. We welcome work focused on sustainability which addresses open scientific or policy questions on either the natural environment, human activities, or both, as well as studies that develop novel methods and datasets in the field of sustainability.