Detecting, classifying, and mapping retail storefronts using street-level imagery
S. Sharifi Noorian (TU Delft - Web Information Systems)
S. Qiu (TU Delft - Web Information Systems)
A. Psyllidis (TU Delft - Knowledge and Intelligence Design)
A. Bozzon (TU Delft - Human-Centred Artificial Intelligence, TU Delft - Web Information Systems)
G.J.P.M. Houben (TU Delft - Web Information Systems)
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Abstract
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 learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output. Our method showed a recall of 89.05% and a precision of 88.22% on a real-world dataset of street-level images, which experimentally demonstrated that our approach achieves human-level accuracy while having a remarkable run-time efficiency compared to methods such as Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot Detector (SSD).