Print Email Facebook Twitter Detecting, classifying, and mapping retail storefronts using street-level imagery Title Detecting, classifying, and mapping retail storefronts using street-level imagery Author Sharifi Noorian, S. (TU Delft Web Information Systems) Qiu, S. (TU Delft Web Information Systems) Psyllidis, A. (TU Delft Internet of Things) Bozzon, A. (TU Delft Human-Centred Artificial Intelligence; TU Delft Web Information Systems) Houben, G.J.P.M. (TU Delft Web Information Systems) Date 2020 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). Subject Convolutional neural networksObject detectionStreet-level imageryUrban data extraction To reference this document use: http://resolver.tudelft.nl/uuid:6e99ca5f-4118-4d40-86e1-26bf71b1a6eb DOI https://doi.org/10.1145/3372278.3390706 Publisher Association for Computing Machinery (ACM) Embargo date 2021-01-01 ISBN 978-1-4503-7087-5 Source ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval Event 10th ACM International Conference on Multimedia Retrieval, 2020-06-26 → 2020-06-29, Dublin, Ireland Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2020 S. Sharifi Noorian, S. Qiu, A. Psyllidis, A. Bozzon, G.J.P.M. Houben Files PDF 3372278.3390706.pdf 2.44 MB Close viewer /islandora/object/uuid:6e99ca5f-4118-4d40-86e1-26bf71b1a6eb/datastream/OBJ/view