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Big Data Analysis in UAV Surveillance for Wildfire Prevention and Management

  • Nikos Athanasis
  • Marinos ThemistocleousEmail author
  • Kostas Kalabokidis
  • Christos Chatzitheodorou
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 341)

Abstract

While wildfires continue to ravage our world, big data analysis aspires to provide solutions to complex problems such as the prevention and management of natural disasters. In this study, we illustrate a state-of-the-art approach towards an enhancement of UAV (Unmanned Aerial Vehicle) surveillance for wildfire prevention and management through big data analysis. Its novelty lies in the instant delivery of images taken from UAVs and the (near) real-time big-data oriented image analysis. Instead of relying on stand-alone computers and time-consuming post-processing of the images, a big data cluster is used and a MapReduce algorithm is applied to identify images from wildfire burning areas. Experiments identified a significant gain regarding the time needed to analyze the data, while the execution time of the image analysis is not affected by the size of the pictures gathered by the UAVs. The integration of UAVs, Big Data components and image analysis provides the means for wildfire prevention and management authorities to follow the proposed methodology to organize their wildfire management plan in a reliable and timely manner. The proposed methodology highlights the role of Geospatial Big Data and is expected to contribute towards a more state-of-the-art knowledge transfer between wildfire confrontation operation centers and firefighting units in the field.

Keywords

Geospatial Big Data Wildfire prevention UAV surveillance 

Notes

Acknowledgments

This work has been partially conducted within the framework of the Greek State Scholarship Foundation (IKY) Scholarship Programs funded by the “Strengthening Post-Doctoral Research” Act from the resources of the OP “Human Resources Development and Lifelong Learning” priority axes 6, 8, 9, and co-financed by the European Social Fund (ESF) and the Greek Government.

References

  1. 1.
    Kalabokidis, K., Athanasis, N., Vasilakos, C., Palaiologou, P.: Porting of a wildfire risk and fire spread application into a cloud computing environment. Int. J. Geogr. Inf. Sci. 28(3), 541–552 (2014)CrossRefGoogle Scholar
  2. 2.
    Hinkley, E.A., Zajkowski, T.: USDA forest service–NASA: unmanned aerial systems demonstrations–pushing the leading edge in fire mapping. Geocarto Int. 26(2), 103–111 (2011)CrossRefGoogle Scholar
  3. 3.
    Allison, R.S., Johnston, J.M., Craig, G., Jennings, S.: Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors 16(8), 1310 (2016)CrossRefGoogle Scholar
  4. 4.
    Gambella, F., et al.: Forest and UAV: a bibliometric review. Contemp. Eng. Sci. 9, 1359–1370 (2016)CrossRefGoogle Scholar
  5. 5.
    Tang, L., Shao, G.: Drone remote sensing for forestry research and practices. J. Forest. Res. 26(4), 791–797 (2015)CrossRefGoogle Scholar
  6. 6.
    Villars, R.L., Olofson, C.W., Eastwood, M.: Big data: what it is and why you should care. White Paper, IDC, 14 (2011)Google Scholar
  7. 7.
    Yuan, C., Zhang, Y., Liu, Z.: A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 45(7), 783–792 (2015)CrossRefGoogle Scholar
  8. 8.
    Pratt, K.S., Murphy, R., Stover, S., Griffin, C.: CONOPS and autonomy recommendations for VTOL small unmanned aerial system based on Hurricane Katrina operations. J. Field Robot. 26(8), 636–650 (2009)CrossRefGoogle Scholar
  9. 9.
    Murphy, R.R., Steimle, E., Griffin, C., Cullins, C., Hall, M., Pratt, K.: Cooperative use of unmanned sea surface and micro aerial vehicles at Hurricane Wilma. J. Field Robot. 25(3), 164–180 (2008)CrossRefGoogle Scholar
  10. 10.
    Nardi, D.: Intelligent systems for emergency response (invited talk). In: Fourth International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disaster (SRMED 2009) (2009)Google Scholar
  11. 11.
    Quaritsch, M., Kruggl, K., Wischounig-Strucl, D., Bhattacharya, S., Shah, M., Rinner, B.: Networked UAVs as aerial sensor network for disaster management applications. e & i Elektrotechnik und Informationstechnik 127(3), 56–63 (2010)CrossRefGoogle Scholar
  12. 12.
    Dunford, R., Michel, K., Gagnage, M., Piégay, H., Trémelo, M.L.: Potential and constraints of unmanned aerial vehicle technology for the characterization of mediterranean riparian forest. Int. J. Remote Sens. 30(19), 4915–4935 (2009)CrossRefGoogle Scholar
  13. 13.
    Merino, L., Caballero, F., Martínez-de-Dios, J.R., Maza, I., Ollero, A.: An unmanned aircraft system for automatic forest fire monitoring and measurement. J. Intell. Rob. Syst. 65(1–4), 533–548 (2012)CrossRefGoogle Scholar
  14. 14.
    Ofli, F., et al.: Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big Data 4(1), 47–59 (2016)CrossRefGoogle Scholar
  15. 15.
    Andrejevic, M., Kelly, G.: Big data surveillance: introduction. Surveill. Soc. 12(2), 185–196 (2014)CrossRefGoogle Scholar
  16. 16.
    Baumann, P., et al.: Big data analytics for earth sciences: the earthserver approach. Int. J. Digit. Earth 9(1), 3–29 (2016)CrossRefGoogle Scholar
  17. 17.
    Nguyen, D.T., Ofli, F., Imran, M., Mitra, P.: Damage assessment from social media imagery data during disasters. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 569–576. ACM (2017)Google Scholar
  18. 18.
    Athanasis, N., Themistocleous, M., Kalabokidis, K.: Wildfire prevention in the era of big data. In: Themistocleous, M., Morabito, V. (eds.) EMCIS 2017. LNBIP, vol. 299, pp. 111–118. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65930-5_9CrossRefGoogle Scholar
  19. 19.
    Athanasis, N., Themistocleous, M., Kalabokidis, K., Papakonstantinou, A., Soulakellis, N., Palaiologou, P.: The emergence of social media for natural disasters management: a big data perspective. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-3/W4, 75–82 (2018).  https://doi.org/10.5194/isprs-archives-XLII-3-W4-75-2018
  20. 20.
    Codella, N.C., Hua, G., Natsev, A., Smith, J.R.: Towards large scale land-cover recognition of satellite images. In: 2011 8th International Conference on Information, Communications and Signal Processing (ICICS), pp. 1–5. IEEE (2011)Google Scholar
  21. 21.
    Zhang, W., et al.: Towards building a multi datacenter infrastructure for massive remote sensing image processing. Concurr. Comput.: Pract. Exp. 25(12), 1798–1812 (2013)CrossRefGoogle Scholar
  22. 22.
    Hadjisophocleous, G.V., Fu, Z.: Literature review of fire risk assessment methodologies. Int. J. Eng. Perform.-Based Fire Codes 6(1), 28–45 (2004)Google Scholar
  23. 23.
    Çelik, T., Ozkaramanlt, H., Demirel, H.: Fire pixel classification using fuzzy logic and statistical color model. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2007, vol. 1, pp. I–1205. IEEE (2007)Google Scholar
  24. 24.
    Yuan, C., Liu, Z., Zhang, Y.: UAV-based forest fire detection and tracking using image processing techniques. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 639–643. IEEE (2015)Google Scholar
  25. 25.
    Yuan, C., Liu, Z., Zhang, Y.: Vision-based forest fire detection in aerial images for firefighting using UAVs. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1200–1205. IEEE (2016)Google Scholar
  26. 26.
    Sweeney, C., Liu, L., Arietta, S., Lawrence, J.: HIPI: a Hadoop image processing interface for image-based mapreduce tasks. University of Virginia, Chris (2011)Google Scholar
  27. 27.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  28. 28.
    Agarwal, V., Abidi, B.R., Koshan, A., Abidi, M.A.: An overview of color constancy algorithms. J. Pattern Recogn. Res. 1, 42–54 (2006)CrossRefGoogle Scholar
  29. 29.
    Connolly, C., Fleiss, T.: A study of efficiency and accuracy in the transformation from RGB to CIELAB color space. IEEE Trans. Image Process. 6(7), 1046–1048 (1997)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of PiraeusPiraeusGreece
  2. 2.University of NicosiaNicosiaCyprus
  3. 3.University of AegeanMytileneGreece

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