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In this work, we propose a novel technique to determine the aesthetic score of a location from social metadata of Flickr photos. In particular, we built machine learning classifiers to predict the class of a location where each class corresponds to a set of locations having equal aesthetic rating. These models are trained on two empirically build datasets containing locations in two different cities (Rome and Paris) where aesthetic ratings of locations were gathered from TripAdvisor.com. In this work we exploit the idea that in a location with higher aesthetic rating, it is more likely for an user to capture a photo and other users are more likely to interact with that photo. Our models achieved as high as 79.48% accuracy (78.60% precision and 79.27% recall) on Rome dataset and 73.78% accuracy(75.62% precision and 78.07% recall) on Paris dataset. The proposed technique can facilitate urban planning, tour planning and recommending aesthetically pleasing paths.

Opportunities to answer many real life queries such as "which surveillance camera has the best view of a moving car in the presence of obstacles?" have become a reality due to the development of location based services and recent advances in 3D modeling of urban environments. In this paper, we investigate the problem of continuously finding the best viewpoint from a set of candidate viewpoints that provides the best view of a moving target in presence of visual obstacles in 2D or 3D space. We propose a query type called k Continuous Maximum Visibility (kCMV) query that ranks k query viewpoints (or locations) from a set of candidate viewpoints in the increasing order of the visibility measure of the target from these viewpoints. We propose two approaches that reduce the set of query locations and obstacles to consider during visibility computation and efficiently update the results as target moves. We conduct extensive experiments to demonstrate the effectiveness and efficiency of our solutions for a moving target in presence of obstacles.

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