Local computers couldn't process millions of satellite images. Parallel ML now maps 50,000+ pollution sources across 1.5M sq miles.
A university research initiative targeting the Indo-Gangetic Plain, where over 400 million people face severe health risks from unmapped air pollution sources.
Local computers could not process the millions of satellite images required to identify specific emission assets like brick kilns. Without scalable...
“Our goals were to be efficient and to get the data out so that there was something tangible for governments and organizations in the IGP region to use. We started with a social goal, and AWS was the means to reach that goal.”
Research organization providing asset-level air pollution detection data and tools.
Cloud computing platform and on-demand infrastructure services.
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