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Challenges of Image-Based Crowd-Sourcing for Situation Awareness in Disaster Management

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Abstract

One of the main issues for authorities in disaster management is to build clear situation awareness that is consistent and accurate in both space and time. Authorities usually have Geographical Information Systems (GIS) for reference data. One current trend of GIS is the use of crowd sourcing, i.e. gather information from the public to build very large databases that would be very costly otherwise. The OpenStreetMap project is one famous example of this. One can easily imagine that, provided some communications networks are available, photographs shot with smartphones could help to build knowledge about the disaster scene. Advantages would be that costly sensor networks would be less necessary, more objectivity would come from pictures than from the public interpretation and that a huge amount of data could be collected very fast. Yet this raises a number of challenges that we will discuss in this paper: the first is geo-referencing of pictures, i.e. locating the pictures received with respect to the current GIS. Even if GPS are more and more common on smartphones, not all pictures will have GPS coordinates and GPS accuracy might not be enough. The second one is the need for a spatio-temporal data model to store and retrieve redundant and uncertain data, the third one is the need for new visualization techniques given the amount of data that will be stored. Finally, legal and ethical issues are raised by the use of massive images.

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References

  1. Arth C, Wagner D, Klopschitz M, Irschara A, Schmalstieg D (2009) Wide area localization on mobile phones. In: ISMAR, Orlando, pp 73–82

    Google Scholar 

  2. Arth C, Klopschitz M, Reitmayr G, Schmalstieg D (2011) Real-time self-localization from panoramic images on mobile devices. In: ISMAR, Basel, pp 37–46

    Google Scholar 

  3. Assilzadeh H, Levy JK, Wang X (2010) Landslide catastrophes and disaster risk reduction: a GIS framework for landslide prevention and management. Remote Sens 2:2259–2273. doi:10.3390/rs2092259

    Article  Google Scholar 

  4. Beard K (2006) Modeling change in space and time: an event based approach. In: Innovations in GIS: dynamic and mobile GIS: investigating change in space and time. Taylor & Francis, Hoboken, pp 55–74

    Google Scholar 

  5. Bioret N (2010) Image geolocazation using a GIS. PhD thesis (in french), École Centrale de Nantes

    Google Scholar 

  6. De Runz C (2008) Imperfection, temps et espace: modélisation, analyse et visualisation dans un SIG archéologique. PhD thesis, université de Reims

    Google Scholar 

  7. Desjardin E, de Runz C, Pargny D, Nocent O (2012) Modélisation d’un SIG archéologique et développement d’outils d’analyse prenant en compte l’imperfection de l’information. RIG 22(3):367–387. doi:10.3166/rig.22.367-387

    Google Scholar 

  8. Duda KA, Abrams M (2012) Aster satellite observations for international disaster management. Proc IEEE 100(10):2798–2811

    Article  Google Scholar 

  9. Friis-Christensen A, Tryfona N, Jensen C (2001) Requirements and research issues in geographic data modeling. In: Proceedings of the 9th ACM international symposium on advances in geographic information systems, Atlanta, pp 2–8

    Google Scholar 

  10. Gupta A, Lamba H, Kumaraguru P, Joshi A (2013) Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: International conference on world wide web companion, WWW’13 Companion, Rio de Janeiro, pp 729–736

    Google Scholar 

  11. Hayes C, Kesan J (2012) At war over CISPA: towards a reasonable balance between privacy and security. Illinois public law research paper (13-03)

    Google Scholar 

  12. He S, Moreau G, Martin J (2012) Footprint-based generalization of 3D building groups at medium level of detail for multi-scale urban visualization. Int J Adv Softw 5(3&4):377–387

    Google Scholar 

  13. Jha MN, Levy J, Gao Y (2008) Advances in remote sensing for oil spill disaster management: state-of-the-art sensors technology for oil spill surveillance. Sensors 8(1):236–255

    Article  Google Scholar 

  14. Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: Proceedings of IEEE and ACM international workshop on augmented reality, San Francisco, pp 85–94. doi:10.1109/IWAR.1999.803809

    Google Scholar 

  15. Klein G, Drummond T (2004) Tightly integrated sensor fusion for robust visual tracking. Image Vis Comput 22(10):769–776

    Article  Google Scholar 

  16. Laplanche F (2002) Conception de projet SIG avec UML. Liège: bulletin de la Société géographique de Liège 42:19–25

    Google Scholar 

  17. Lee GA, Dünser A, Kim S, Billinghurst M (2012) CityViewAR: a mobile outdoor AR application for city visualization. In: ISMAR AHM, Atlanta, pp 57–64

    Google Scholar 

  18. Minout M (2007) Modélisation des Aspects Temporels dans les Bases de Données Spatiales. PhD thesis, université libre de Bruxelles

    Google Scholar 

  19. Miralles A (2005) Ingénierie des modèles pour les applications environnementales. PhD thesis, Montpellier university

    Google Scholar 

  20. OpenStreetMap. http://www.openstreetmap.org

  21. Parent C, Spaccapietra S, Zimanyi E (2006) Conceptual modeling for traditional and spatio-temporal applications: the MADS approach. Springer, Berlin

    Google Scholar 

  22. Parent C, Spaccapietra S, Zimanyi E (2009) Semantic modeling for geographic information systems. In: Encyclopedia of database systems. Springer, New York, pp 2571–2576

    Google Scholar 

  23. Pelekis N, Theodoulidis B, Kopanakis I, Theodoridis Y (2005) Literature review of spatio-temporal database models. Knowl Eng Rev 19:235–274

    Google Scholar 

  24. Regidor E (2004) The use of personal data from medical records and biological materials: ethical perspectives and the basis for legal restrictions in health research. Soc Sci Med 59(9):1975–1984

    Article  Google Scholar 

  25. Reitmayr G, Drummond T (2006) Going out: robust model-based tracking for outdoor augmented reality. In: Proceedings of IEEE ISMAR’06, Santa Barbara, pp 109–118. doi:10.1109/ISMAR.2006.297801

  26. Richter S, Hammitzsch M (2013) Development of an android app for notification and reporting of natural disaster such as earthquakes and tsunamis. General Assembly European Geosciences Union

    Google Scholar 

  27. Roche S, Propeck-Zimmermann E, Mericskay B (2013) GeoWeb and crisis management: issues and perspectives of volunteered geographic information. GeoJournal 78:21–40. doi:10.1007/s10708-011-9423-9

    Article  Google Scholar 

  28. Seo B-K, Park J-I, Park H (2011) Camera tracking using partially modeled 3-D objects with scene textures. In: ISVRI, Singapore, pp 293–298

    Google Scholar 

  29. Simon G (2011) Tracking-by-synthesis using point features and pyramidal blurring. In: ISMAR, Basel, pp 85–92. doi:10.1109/ISMAR.2011.6162875

    Google Scholar 

  30. Simon G, Fitzgibbon A, Zisserman A (2000) Markerless tracking using planar structures in the scene. In: Proceedings of international symposium on augmented reality, Darmstadt, pp 120–128

    Google Scholar 

  31. Steinhoff U, Omercevic D, Perko R, Schiele B, Leonardis A (2007) How computer vision can help in outdoor positioning. Ambient Intell Lect Notes Comput Sci 4794:124–141. doi:10.1007/978-3-540-76652-0_8

    Article  Google Scholar 

  32. Tan DR (1999) Personal privacy in the information age: comparison of internet data protection regulations in the United Stats and European union. Loy LA Int’l & Comp LJ 21:661

    Google Scholar 

  33. Tronin AA (2009) Satellite remote sensing in seismology. A review. Remote Sens 2(1):124–150

    Article  Google Scholar 

  34. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends Comput Graph Vis 3(3):177–280

    Article  Google Scholar 

  35. Uchiyama H, Saito H, Nivesse V, Servières M, Moreau G (2008) AR representation system for 3D GIS based on camera pose estimation using distribution of intersections. In: International Conference on Artificial Reality and Telexistence (ICAT), Yokohama, pp 218–225

    Google Scholar 

  36. Uddin K, Gurung DR, Giriraj A, Shrestha B (2013) Application of remote sensing and GIS for flood hazard management: a case study from Sindh Province, Pakistan. Am J Geogr Inf Syst 2(1):1–5. doi:10.5923/j.ajgis.20130201.01

    Google Scholar 

  37. U.S. District Court for the Southern District of New York, Case agence france-presse v. morel, No. 10-02730, 11 (2013)

    Google Scholar 

  38. Vacchetti L, Lepetit V, Fua P (2003) Fusing online and offline information for stable 3D tracking in real-time. In: CVPR, II, Madison, vol 2, pp 241–248. doi:10.1109/CVPR.2003.1211476

    Google Scholar 

  39. Ventura J, Hollerer T (2012) Wide-area scene mapping for mobile visual tracking. In: ISMAR, Atlanta, pp 3–12

    Google Scholar 

  40. Voigt S, Kemper T, Riedlinger T, Kiefl R, Scholte K, Mehl H (2007) Satellite image analysis for disaster and crisis-management support. Geosci Remote Sens 45(6):1520–1528

    Article  Google Scholar 

  41. Wagner D, Reitmayr G, Mulloni A, Drummond T, Schmalstieg D (2010) Real-time detection and tracking for augmented reality on mobile phones. IEEE Trans Vis Comput Graph 16(3):355–368

    Article  Google Scholar 

  42. Wolfson S, Lease M (2011) Look before you leap: legal pitfalls of crowdsourcing. Am Soc Inf Sci Technol 48(1):1–10

    Article  Google Scholar 

  43. Zaki C (2011) Modélisation Spatio-temporelle multi-échelle des données dans un SIG urbain. PhD thesis, École Centrale Nantes

    Google Scholar 

  44. Zhang F, Tourre V, Moreau G (2013) A general strategy for semantic levels of details in 3D urban visualization. In: Eurographics workshop on urban data modelling and visualisation, Girona, pp 33–36

    Google Scholar 

  45. Zhu S, Morin L, Pressigout M, Moreau G, Servières M (2013) Video/GIS registration system based on skyline matching method. In: IEEE ICIP, Melbourne, pp 3632–3636

    Google Scholar 

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Correspondence to Guillaume Moreau .

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Moreau, G., Servières, M., Normand, JM., Magnin, M. (2014). Challenges of Image-Based Crowd-Sourcing for Situation Awareness in Disaster Management. In: Teodorescu, HN., Kirschenbaum, A., Cojocaru, S., Bruderlein, C. (eds) Improving Disaster Resilience and Mitigation - IT Means and Tools. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9136-6_7

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  • DOI: https://doi.org/10.1007/978-94-017-9136-6_7

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