Detecting, classifying, and mapping retail storefronts using street-level imagery

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep...

ST-Sem: A Multimodal Method for Points-of-Interest Classification Using Street-Level Imagery

Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general mor- phological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and iden- tity of a POI. Recent advancements in computer vision could...

A Convolutional Neural Network-Based Model for Predicting The Perceived Attractiveness of Urban Places

An understanding of how people perceive attractive or unattractive places in cities is vitally important to urban planning and policy making. Given the subjective nature of human perception and the ambiguous character of attractiveness as an attribute of urban places, it is challenging to quantify and reliably assess the extent to which a place...