Processing Collections of Geo-Referenced Images for Natural Disasters

  • Fernando Loor CONICET, Universidad Nacional de San Luis, Argentina.
  • Veronica Gil-Costa CONICET, Universidad Nacional de San Luis, Argentina.
  • Mauricio Marin Universidad de Santiago de Chile, Chile.
Keywords: Geo-referenced images, support platforms for natural disaster, P2P network

Abstract

After disaster strikes, emergency response teams need to work fast. In this context, crowdsourcing has emerged as a powerful mechanism where volunteers can help to process different tasks such as processing complex images using labeling and classification techniques. In this work we propose to address the  problem of how to efficiently process large volumes of georeferenced images using crowdsourcing in the context of high risk such as natural disasters. Research on citizen science and crowdsourcing indicates that volunteers should be able to contribute in a useful way with a limited time to a project, supported by the results of usability studies. We present the design of a platform for real-time processing of georeferenced images. In particular, we focus on the interaction between the crowdsourcing and the volunteers connected to a P2P network.

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References

M. R. Evans, D. Oliver, X. Zhou, and S. Shekhar, “Spatial big data : Case studies on volume , velocity , and variety,” inMobiDE ’12 Proceedings of the Eleventh ACM InternationalWorkshop on Data Engineering for Wireless and Mobile Access, 2012.

S. Newsam, “Crowdsourcing what is where: Community-contributed photos as volunteered geographic information,” IEEE MultiMedia, vol. 17, pp. 36–45, Oct 2010.

S. Thorvaldsdottir, E. Birgisson, and R. Sigbjornsson, “Interactive on-site and remote damage assessment for urban search and rescue,” Earthquake Spectra, vol. 27, no. S1, pp. S239–S250, 2011.

S. Shekhar, V. Gunturi, M. R. Evans, and K. Yang, “Spatial big-data challenges intersecting mobility and cloud computing,” in Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE ’12, (New York, NY, USA), pp. 1–6, ACM, 2012.

M. Sester, J. Jokar Arsanjani, R. Klammer, D. Burghardt, and J.-H. Haunert, Integrating and Generalising Volunteered Geographic Information, pp. 119–155. Cham: Springer International Publishing, 2014.

“Where is the phrase “80% of data is geographic” from?.” Available at: http://www.gislounge.com/80-percent-data-is-geographic/. Accessed on 2018-11-10.

M. Choy, J.-G. Lee, G. Gweon, and D. Kim, “Glaucus: Exploiting the wisdom of crowds for location-based queries in mobile environments,” in Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, pp. 61–70, 2014.

D. Becker and S. Bendett, “Crowdsourcing solutions for disaster response: Examples and lessons for the us government,” Procedia Engineering, vol. 107, pp. 27 – 33, 2015. Humanitarian Technology: Science, Systems and Global Impact 2015, HumTech2015.

L. Barrington, S. Ghosh,M. Greene, S. Har-Noy, J. Berger, S. Gill, A. Lin, and C. Huyck, “Crowdsourcing earthquake damage assessment using remote sensing imagery,” Annals of Geophysics, vol. 54, no. 6, 2012.

J.-G. Lee and M. Kang, “Geospatial big data: Challenges and opportunities,” Big Data Re- search, vol. 2, no. 2, pp. 74 – 81, 2015. Visions on Big Data.

F. Ofli, P. Meier, M. Imran, C. Castillo, D. Tuia, N. Rey, J. Briant, P. Millet, F. Reinhard, M. Parkan, and S. Joost, “Combining human computing and machine learning to make sense of big (aerial) data for disaster response,” Big Data, vol. 4, no. 1, pp. 47–59, 2016. PMID: 27441584.

C. Smith, A Case Study of Crowdsourcing Imagery Coding in Natural Disasters, pp. 217–230. Cham: Springer International Publishing, 2017.

N. Witjes, P. Olbrich, and I. Rebasso, Big Data from Outer Space: Opportunities and Challenges for Crisis Response, pp. 215–225. Vienna: Springer Vienna, 2017.

C. Turk, “Cartographica incognita: ‘dijital jedis’, satellite salvation and the mysteries of the ‘missing maps’,” The Cartographic Journal, vol. 54, no. 1, pp. 14–23, 2017.

T. Onorati and P. D´ıaz, “Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data,” in SpringerPlus, 2016.

P. D´ıaz, J. M. Carroll, and I. Aedo, “Coproduction as an approach to technology-mediated citizen participation in emergency management,” Future Internet, vol. 8, no. 3, 2016.

A. Rowstron and P. Druschel, “Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems,” in Middleware 2001 (R. Guerraoui, ed.), (Berlin, Heidelberg), pp. 329–350, Springer Berlin Heidelberg, 2001.

M. Marzolla, “Libcppsim: a simula-like, portable process-oriented simulation library in c++,” ESM, vol. -, no. 1, pp. 222—-227, 2004.

Published
2018-12-12
How to Cite
Loor, F., Gil-Costa, V., & Marin, M. (2018). Processing Collections of Geo-Referenced Images for Natural Disasters. Journal of Computer Science and Technology, 18(03), e22. https://doi.org/10.24215/16666038.18.e22
Section
Original Articles