A new project by Carnegie Mellon University researchers provides journalists, citizen scientists, and other researchers with the ability to quickly scan large geographical regions for specific visual features.
Simply click on a feature in the satellite imagery – a baseball diamond, cul-de-sac, tennis court – and Terrapattern will find other things that look similar in the area and pinpoint them on the map.
Using a deep learning neural network trained for five days on an NVIDIA GeForce GPU, their model will look at small squares of the landscape and, comparing those patterns to a huge database of tagged map features from OpenStreetMap, it learned to associate them with certain concepts.
Currently, Terrapattern is limited to Pittsburgh, San Francisco, New York City and Detroit, but access to more cities is coming soon.
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Open-Access Visual Search Tool for Satellite Imagery
May 26, 2016
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AI-Generated Summary
- Researchers at Carnegie Mellon University have developed a tool called Terrapattern that allows users to search for specific visual features in satellite imagery across large geographical areas.
- Terrapattern uses a deep learning neural network trained on an NVIDIA GeForce GPU to identify patterns in the landscape and associate them with certain concepts based on a database of tagged map features from OpenStreetMap.
- The tool is currently available for use in Pittsburgh, San Francisco, New York City, and Detroit, with more cities to be added soon.
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