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| Computer Vision |
Photo Geolocation using Street and Restaurant Data
Team: Tianyu Su, Zhuangyuan Fan
Supervisor: Prof. William T. Freeman,
Prof. Antonio Torralba, Prof. Phillip Isola
# Computer Vision, Deep Learning, Mapping
In computer vision, the photo geolocation problem has been usually approached at a global scale or regional
scale. In this project, we derive knowledge from urban studies and present a classification based method looking into neighborhood scale photo geotagging.
We subdivide Boston Chinatown into multiple geographic cells and train a deep network using 20K google street view images labeled with local restaurant density and street speed limit. We show that this multitask model could output a probability distribution over a couple of cells in the neighborhood.
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