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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)
182 Downloads (Pure)


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).

Original languageEnglish
Title of host publicationICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Electronic)978-1-4503-7087-5
Publication statusPublished - 2020
Event10th ACM International Conference on Multimedia Retrieval - Dublin, Ireland
Duration: 26 Jun 202029 Jun 2020


Conference10th ACM International Conference on Multimedia Retrieval
Abbreviated titleICMR 2020
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project 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.


  • Convolutional neural networks
  • Object detection
  • Street-level imagery
  • Urban data extraction


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