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Mobile augmented reality for environmental monitoring

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Abstract

In response to dramatic changes in the environment, and supported by advances in wireless networking, pervasive sensor networks have become a common tool for environmental monitoring. However, tools for on-site visualization and interactive exploration of environmental data are still inadequate for domain experts. Current solutions are generally limited to tabular data, basic 2D plots, or standard 2D GIS tools designed for the desktop and not adapted to mobile use. In this paper, we introduce a novel augmented reality platform for 3D mobile visualization of environmental data. Following a user-centered design approach, we analyze processes, tasks, and requirements of on-site visualization tools for environmental experts. We present our multilayer infrastructure and the mobile augmented reality platform that leverages visualization of georeferenced sensor measurement and simulation data in a seamless integrated view of the environment.

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Notes

  1. http://www.opengeospatial.org/standards/swes.

  2. http://slfsmm.indefero.net/p/meteoio/.

  3. École Polytechnique Fédérale de Lausanne.

References

  1. Aberer K, Alonso G, Barrenetxea G, Beutel J, Bovay J, Dubois-Ferrière H, Kossmann D, Parlange M, Thiele L, Vetterli M (2007) Infrastructures for a Smart Earth—the Swiss NCCR-MICS initiative. Prax der Informationsverarbeitung und Kommun 30(1):20–25

    Article  Google Scholar 

  2. Baggi S, Schweizer J (2009) First results on characteristics of wet snow avalanche activity in a high alpine valley. Nat Hazards 50(1):97–108

    Article  Google Scholar 

  3. Beyer H, Holtzblatt K (1998) Contextual design: defining customer-centered systems. Morgan Kaufmann, Burlington

    Google Scholar 

  4. Bogue R (2008) Environmental sensing: strategies, technologies and applications. Sens Rev 28(4):275–282

    Article  Google Scholar 

  5. Burns M, Klawe J, Rusinkiewicz S, Finkelstein A, DeCarlo D (2005) Line drawings from volume data. ACM Trans Graph TOG 24(3):512

    Article  Google Scholar 

  6. ESRI (2004) ArcPad®: Mobile GIS. Technical report, ESRI

  7. Falk MG, Denham RJ, Mengersen KL (2009) Spatially stratified sampling using auxiliary information for geostatistical mapping. Environ Ecol Stat 18(1):93–108

    Google Scholar 

  8. Hart JK, Martinez K (2006) Environmental sensor networks: a revolution in the earth system science? Earth Sci Rev 78(3–4):177–191

    Article  Google Scholar 

  9. Hengl T (2006) Finding the right pixel size. Comput Geosci 32(9):1283–1298

    Article  Google Scholar 

  10. Hilbich C, Hauck C, Hoelzle M, Scherler M, Schudel L, Völksch I, Mühll DV, Mäusbacher R (2008) Monitoring mountain permafrost evolution using electrical resistivity tomography: a 7-year study of seasonal, annual, and long-term variations at Schilthorn, Swiss Alps. J Geophys Res 113(F1):1–12

    Article  Google Scholar 

  11. Höllerer T, Feiner SK (2004) Mobile augmented reality. Telegeoinformatics LocationBased Computing and Services, pp 1–39

  12. Jeung H, Sarni S, Paparrizos I, Sathe S, Aberer K, Dawes N, Papaioannou TG, Lehning M (2010) Effective metadata management in federated sensor networks. 2010 IEEE international conference on sensor networks ubiquitous and trustworthy computing, pp 107–114

  13. Kim S, Paulos E (2009) inAir: measuring and visualizing indoor air quality. In: Proceedings of the 28th international conference on human factors in computing systems CHI 10. ACM Press, pp 1861–1870

  14. King GR, Piekarski W, Thomas BH (2005) ARVino—outdoor augmented reality visualisation of viticulture GIS data. In: Werner B (ed), Proceedings of the 4th IEEE/ACM international symposium on mixed and augmented reality. IEEE Computer Society, pp 52–55

  15. Kruijff E, Mendez E, Veas E, Gruenewald T (2010) On-site monitoring of environmental processes using mobile augmented reality (HYDROSYS). In envip 2010

  16. Lehning M, Völksch I, Gustafsson D, Nguyen TA, Stähli M, Zappa M (2006) ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology. Hydrol Process 2128(May 2005):2111–2128

    Google Scholar 

  17. Lin C-R, Loftin RB (1998) Application of virtual reality in the interpretation of geoscience data. In: Proceedings of the ACM symposium on virtual reality software and technology 1998 VRST 98. ACM Press, pp 187–194

  18. Liston GE, Sturm M (1998) A snow-transport model for complex terrain. J Glaciol 44(148):498–516

    Google Scholar 

  19. Mitas L, Mitasova H (1998) Distributed soil erosion simulation for effective erosion prevention. Water Resour Res 34(3):505–516

    Article  Google Scholar 

  20. Mitterer C, Hirashima H, Schweizer J (2011) Wet-snow instabilities: comparison of measured and modelled liquid water content and snow stratigraphy. Ann Glaciol 52(58):201–208

    Article  Google Scholar 

  21. Mitterer C, Mott R, Schweizer J (2009) Observations and analysis of two wet-snow avalanche cycles. In: International snow science workshop ISSW, number April 2008. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, pp 262–266

  22. Nocke T, Sterzel T, Böttinger M, Wrobel M (2008) Visualization of climate and climate change data: an overview. In: Ehlers EEA et al. (eds) Digital earth summit on geoinformatics 2008 tools for global change research ISDE08, Wichmann, Heidelberg, pp 226–232

  23. Paxton M, Benford S (2009) Experiences of participatory sensing in the wild. In: Proceedings of the 11th international conference on ubiquitous computing ubicomp 09, p 265

  24. Schuler D, Namioka A (1993) Participatory design: principles and practices. Routledge, London

    Google Scholar 

  25. Schweizer J, Jamieson JB, Schneebeli M (2003) Snow avalanche formation. Rev Geophys 41(4):1016

    Article  Google Scholar 

  26. Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings 1996 IEEE symposium on visual languages, pp 336–343

  27. Simoni S, Zanotti F, Bertoldi G, Rigon R (2008) Modelling the probability of occurrence of shallow landslides and channelized debris flows using geotop-fs. Hydrol Process 22(4):532–545

    Article  Google Scholar 

  28. Techel F, Pielmeier C, Schneebeli M (2011) Microstructural resistance of snow following first wetting. Cold Reg Sci Technol 65(3):382–391

    Article  Google Scholar 

  29. Thomas JJ, Cook KA (2005) Illuminating the path: the research and development agenda for visual analytics, vol 54. IEEE

  30. Veas E, Grasset R, Kruijff E, Schmalstieg D (2012) Extended overview techniques for outdoor augmented reality. IEEE Trans Vis Comput Graph 18:565–572

    Article  Google Scholar 

  31. Veas EE, Mulloni A, Kruijff E, Regenbrecht H, Schmalstieg D (2010) Techniques for view transition in multi-camera outdoor environments. In: Graphics interface 2010 (GI’10), Toronto, Ont., 2010. Canadian Information Processing Society, pp 193–200

  32. White S (2009) SiteLens: situated visualization techniques for urban site visits. Evaluation, pp 1117–1120

  33. Yang J, Zhang C, Li X, Huang Y, Fu S, Acevedo MF (2009) Integration of wireless sensor networks in environmental monitoring cyber infrastructure. Wirel Netw 16(4):1091–1108

    Article  Google Scholar 

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Acknowledgments

This work is partially funded by the EC 7th Framework project HYDROSYS (224416, DG-INFSO).

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Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16a, 8010, Graz, Austria

    Eduardo Veas, Raphaël Grasset & Dieter Schmalstieg

  2. School of Engineering, Aalto University, Niemenkatu 73, 15140, Lahti, Finland

    Ioan Ferencik

  3. WSL Institute for Snow and Avalanche Research, SLF, Flüelastr. 11, 7260, Davos Dorf, Switzerland

    Thomas Grünewald

Authors
  1. Eduardo Veas
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  2. Raphaël Grasset
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  3. Ioan Ferencik
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  4. Thomas Grünewald
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  5. Dieter Schmalstieg
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Correspondence to Eduardo Veas.

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Veas, E., Grasset, R., Ferencik, I. et al. Mobile augmented reality for environmental monitoring. Pers Ubiquit Comput 17, 1515–1531 (2013). https://doi.org/10.1007/s00779-012-0597-z

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