Table of Contents
Urban Sci., Volume 1, Issue 2 (June 2017) – 11 articles
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Cover Story (view full-size image) The cover image illustrates urban mapping through street-level images as implemented by Ticinum [...] Read more. The cover image illustrates urban mapping through street-level images as implemented by Ticinum Aerospace. Starting from a public map of the area of interest, such as an OpenStreetMap layer, the system defines a georeferenced “visit path”, along which street-side building pictures are sought for, and harvested from, publicly accessible repositories. The retrieved georeferenced images are fed into an opportunely trained deep neural network. This latter consequently finds determining features and labels the building according to a given taxonomy, in addition to determining specific parameters such as floor count, resulting in a remarkably enriched GIS layer being output. This paper shows how deep learning enables leveraging on the wealth of available crowdsourced pictures to benefit practical applications including, for example, enhanced exposure models for risk assessment, or real estate valuation. View this paper.