Dissecting urban noises from heterogeneous geo-social media and sensor data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Geo-social media services, such as Foursquare and Flickr, provide rich data that sensors various urban activities of hu-man beings from geographical, mobility, visual, and social aspects. While noise pollution in modern cities is getting worse and sound sensors are sparse and costly, it is highly demanded to infer and analyze the noise at any region in urban areas. In this paper, we aim to leverage heterogeneous geo-social sensor data on Foursquare, Flickr, and Gowalla, to dissect urban noises for every regions in a city. Using NYC 311 noise complaint records as the approximation of urban noises generated by regions, we propose a novel unsuper-vised framework that integrates the extracted geographical, mobility, visual, and social features to infer the noise compo-sition for regions and time intervals of interest in NYC. Ex-perimental results show that our system can achieve promis-ing results with substantially few training data, compared to state-of-The-Art methods.

Original languageEnglish
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1103-1106
Number of pages4
ISBN (Electronic)9781450334594
DOIs
Publication statusPublished - 2015 Oct 13
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: 2015 Oct 262015 Oct 30

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other23rd ACM International Conference on Multimedia, MM 2015
CountryAustralia
CityBrisbane
Period15-10-2615-10-30

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Hsieh, H-P., Yan, R., & Li, C-T. (2015). Dissecting urban noises from heterogeneous geo-social media and sensor data. In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference (pp. 1103-1106). (MM 2015 - Proceedings of the 2015 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2733373.2806292