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
- Carnegie Mellon University researchers created Terrapattern, a tool that lets users click a feature in satellite imagery to find visually similar features across a geographic region.
- A deep learning neural network trained for five days on an NVIDIA GeForce GPU analyzes small landscape squares and matches patterns against a database of tagged map features from OpenStreetMap.
- The system currently covers Pittsburgh, San Francisco, New York City and Detroit, with more cities planned.
Next Step
- Read the TechCrunch article for additional details on the project.
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