The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 331–340, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-331-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 331–340, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-331-2019

  22 Aug 2019

22 Aug 2019

CROWD4EMS: A CROWDSOURCING PLATFORM FOR GATHERING AND GEOLOCATING SOCIAL MEDIA CONTENT IN DISASTER RESPONSE

A. Ravi Shankar1, J. L. Fernandez-Marquez1, B. Pernici2, G. Scalia2, M. R. Mondardini3, and G. Serugendo1 A. Ravi Shankar et al.
  • 1Centre Universitaire d’Informatics, University of Geneva, Switzerland
  • 2Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
  • 3Citizen Science Centre, ETH - UZH, Zurich, Switzerland

Keywords: Crowdsourcing, Social Media, Machine Learning Geolocation, Disaster Response

Abstract. Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location. The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event.