Environmental sustainability depends on very large geospatial data sets, particularly satellite imagery and climate forecasts. But working with geospatial data – whether it’s large or not – is a deep skill set unto itself, and working with very large data – whether it’s geospatial or not – is its own field of expertise as well. Consequently, the niche expertise required in GIS and distributed computing creates a huge barrier between this invaluable data and the sustainability practitioners who need it. Microsoft’s Planetary Computer aims to lower that barrier, by combining (1) a 25PB catalog of analysis-ready geospatial data, in consistent file formats, in a single data center, (2) an API that facilitates spatiotemporal querying over that data, and (3) a computing environment that simplifies distributed computing workloads. Of course, files and distributed computing don’t address environmental issues directly, so Microsoft is also partnering closely with the sustainability community to build applications that put the Planetary Computer to work for sustainability decision-making.
This talk will highlight features and capabilities of Microsoft's Planetary Computer and showcase selected applications. Possible collaboration options will also be discussed.
Speaker: Dr. Dan Morris
Dan Morris is a Principal Scientist with Microsoft’s AI for Earth program, focused on accelerating innovation at the intersection of machine learning and environmental sustainability, particularly through the Planetary Computer platform. When he’s not moving geospatial data around on the cloud, his work includes computer vision applications in wildlife conservation, for example the AI for Earth Camera Trap Image Processing API. Prior to joining AI for Earth, he worked in Microsoft’s Medical Devices Group, developing signal processing and machine learning techniques for cardiovascular health monitoring, along with earlier work on signal processing and machine learning for input systems, making medical information more useful to hospital patients, automatic exercise analysis from wearable sensors, and generating musical accompaniment for vocal melodies (the “Songsmith” project). Before coming to Microsoft, he studied neuroscience at Brown, and developed brain-computer interfaces for research and clinical environments. His PhD work at Stanford focused on haptics and physical simulation for virtual surgery.
