The final work of my graduate program at Wayne State was a masters essay on TO(E)D for Rust Belt cities, since this has been the reason why updates have been so sparse as of late, I felt that it was only fair to post it on The Dispatch. Abstract Many American industrial, or “Rust Belt” cities in […]
Some really cool stuff is happening over at Carnegie Mellon University.
Given a large repository of geotagged imagery, we seek to automatically find visual elements, e.g. windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle. In addition, we face a hard search problem: given all possible patches in all images, which of them are both frequently occurring and geographically informative? To address these issues, we propose to use a discriminative clustering approach able to take into account the weak geographic supervision. We show that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner. We demonstrate that these elements are visually interpretable and perceptually geo-informative. The discovered visual elements can also support a variety of computational geography tasks, such as mapping architectural correspondences and influences within and across cities, finding representative elements at different geo-spatial scales, and geographically-informed image retrieval.
Hats off to Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic and Alexei A. Efros for their work on this project. For more info, check out the project website. There’s even a full download of their report. Image courtesy of Jazzbery Blue, who has lots of cool stuff for sale here.
This article is licensed under a Creative Commons Attribution 3.0 Unported License. Click here to view the Urbanist Dispatch Attribution Policy for articles and see how we give back to the community.