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2017 GTC San Jose

S7483 - SpaceNet Satellite Imagery Deep Learning Implementations and Performance

Session Speakers
Session Description

The commercialization of the geospatial industry has led to an explosive amount of data being collected to characterize our changing planet. One area for innovation is the application of computer vision and deep learning to extract information from satellite imagery at scale. SpaceNet's objective is to release remote sensing data (for example, satellite imagery) to the public to enable developers and data scientists. Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. We believe that advancing automated feature extraction techniques will serve important downstream uses of map data, including humanitarian and disaster response, as recently observed by the need to map buildings in Haiti during the response to Hurricane Matthew. Furthermore, we think that solving this challenge is an important stepping stone to unleashing the power of advanced computer vision algorithms applied to a variety of remote sensing data applications in both the public and private sector.


Additional Session Information
All
Talk
Deep Learning and AI Federal
Defense Government / National Labs
50 minutes
Session Schedule