Crowd-Mapping Urban Objects from Street-Level Imagery

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

14 Citations (Scopus)
570 Downloads (Pure)


Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for generating crowdsourced object annotations from street-level imagery, in combination with object density and geo-location estimation techniques to enable the enumeration and geo-tagging of urban objects. To address both the coverage and precision of the mapped objects within budget constraints, we design a scheduling strategy for micro-task prioritization, aggregation, and assignment to crowd workers. We experimentally demonstrate the feasibility of our approach through a use case pertaining to the mapping of street trees in New York City and Amsterdam. We show that anonymous crowds can achieve high recall (up to 80%) and precision (up to 68%), with geo-location precision of approximately 3m. We also show that similar performance could be achieved at city scale, possibly with stringent budget constraints.
Original languageEnglish
Title of host publicationProceedings of the 2019 World Wide Web Conference
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)978-1-4503-6674-8
Publication statusPublished - 2019
EventWWW 2019 : The Web Conference 2019, 30 years of the web - San Francisco, CA, United States
Duration: 13 May 201917 May 2019
Conference number: 30


ConferenceWWW 2019
Abbreviated titleWWW'19
Country/TerritoryUnited States
CitySan Francisco, CA


  • Crowd-Mapping
  • Street-Level Imagery
  • Task Scheduling
  • Crowdsourcing
  • Urban Objects


Dive into the research topics of 'Crowd-Mapping Urban Objects from Street-Level Imagery'. Together they form a unique fingerprint.

Cite this