Crowd-Mapping Urban Objects from Street-Level Imagery

Crowd-Mapping Urban Objects from Street-Level Imagery

Qiu, S. (TU Delft Web Information Systems)
Psyllidis, A. (TU Delft Web Information Systems)
Bozzon, A. (TU Delft Web Information Systems)
Houben, G.J.P.M. (TU Delft Web Information Systems)

2019

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.

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

http://resolver.tudelft.nl/uuid:eb49fead-be26-450a-8f46-2850877c632c

https://doi.org/10.1145/3308558.3313651

Association for Computing Machinery (ACM), New York, NY

978-1-4503-6674-8

Proceedings of the 2019 World Wide Web Conference

WWW 2019, 2019-05-13 → 2019-05-17, San Francisco, CA, United States

Institutional Repository

conference paper

© 2019 S. Qiu, A. Psyllidis, A. Bozzon, G.J.P.M. Houben