Machine learning for Earth Observation: Emissions, mitigation and sequestration
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Constellations of satellites such as the SENTINELs provide almost daily coverage of the earth using a variety of sensors: multispectral and hyperspectral images, synthetic aperture radar, LIDAR, etc. Recent advances in machine learning allow us to process these images at planet scale on a near real-time basis and to paint a very broad picture of key climate related topics such as greenhouse gas emissions, wildfire risk, flooding or carbon sequestration in forests. This talk will cover some of these examples in depth, illustrating some key open numerical challenge.
This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.