Parkour Spot ID: Feature Matching in Satellite and Street view images using Deep Learning
This addresses the challenge of finding specific, unlisted locations like Parkour spots for urban explorers or athletes, but it is incremental as it applies existing machine vision methods to a new domain.
The paper tackled the problem of locating unindexed places by classifying locations using satellite and street view images, resulting in the discovery of over 25 new Parkour spots with a true positive rate above 60%.
How to find places that are not indexed by Google Maps? We propose an intuitive method and framework to locate places based on their distinctive spatial features. The method uses satellite and street view images in machine vision approaches to classify locations. If we can classify locations, we just need to repeat for non-overlapping locations in our area of interest. We assess the proposed system in finding Parkour spots in the campus of Arizona State University. The results are very satisfactory, having found more than 25 new Parkour spots, with a rate of true positives above 60%.