Towards Air Quality Estimation Using Collected Multimodal Environmental Data

  • Anastasia MoumtzidouEmail author
  • Symeon Papadopoulos
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
  • Konstantinos Kourtidis
  • George Hloupis
  • Ilias Stavrakas
  • Konstantina Papachristopoulou
  • Christodoulos Keratidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10078)


This paper presents an open platform, which collects multimodal environmental data related to air quality from several sources including official open sources, social media and citizens. Collecting and fusing different sources of air quality data into a unified air quality indicator is a highly challenging problem, leveraging recent advances in image analysis, open hardware, machine learning and data fusion. The collection of data from multiple sources aims at having complementary information, which is expected to result in increased geographical coverage and temporal granularity of air quality data. This diversity of sources constitutes also the main novelty of the platform presented compared with the existing applications.


Environmental data Air quality Multimodal Collection 



This work is partially funded by the European Commission under the contract number H2020-688363 hackAIR.


  1. 1.
  2. 2.
    Aiello, L.M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., Jaimes, A.: Sensing trending topics in twitter. IEEE Trans. Multimedia 15(6), 1268–1282 (2013)CrossRefGoogle Scholar
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
    Chen, H., Fan, H., Chau, M., Zeng, D.: Metaspider: meta-searching and categorization on the web. J. Am. Soc. Inform. Sci. Technol. 52(13), 1134–1147 (2001)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Clean Air Nation (greenpeace India).
  15. 15.
    Cox, S., et al.: Observations and measurements-xml implementation. OGC document (2011)Google Scholar
  16. 16.
  17. 17.
    Denby, B., Schaap, M., Segers, A., Builtjes, P., Horálek, J.: Comparison of two data assimilation methods for assessing PM\(_{10}\) exceedances on the European scale. Atmos. Environ. 42(30), 7122–7134 (2008). CrossRefGoogle Scholar
  18. 18.
    Epitropou, V., Karatzas, K.D., Bassoukos, A., Kukkonen, J., Balk, T.: A new environmental image processing method for chemical weather forecasts in Europe. In: Golinska, P., Fertsch, M., Marx-Gómez, J. (eds.) Information Technologies in Environmental Engineering. Environmental Science and Engineering, vol. 3, pp. 781–791. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
  20. 20.
  21. 21.
    Johansson, L., Epitropou, V., Karatzas, K., Karppinen, A., Wanner, L., Vrochidis, S., Bassoukos, A., Kukkonen, J., Kompatsiaris, I.: Fusion of meteorological and air quality data extracted from the web for personalized environmental information services. Environ. Model. Softw. 64, 143–155 (2015)CrossRefGoogle Scholar
  22. 22.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  23. 23.
    Markatopoulou, F., Mezaris, V., Pittaras, N., Patras, I.: Local features and a two-layer stacking architecture for semantic concept detection in video. IEEE Trans. Emerg. Top. Comput. 3(2), 193–204 (2015)CrossRefGoogle Scholar
  24. 24.
    Na, A., Priest, M.: Sensor observation service. Implementation Standard OGC (2007)Google Scholar
  25. 25.
    Oyama, S., Kokubo, T., Ishida, T.: Domain-specific web search with keyword spices. IEEE Trans. Knowl. Data Eng. 16(1), 17–27 (2004)CrossRefGoogle Scholar
  26. 26.
  27. 27.
  28. 28.
  29. 29.
    Ricchiazzi, P., Yang, S., Gautier, C., Sowle, D.: Sbdart: a research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere. Bull. Am. Meteorol. Soc. 79(10), 2101–2114 (1998)CrossRefGoogle Scholar
  30. 30.
    Saito, M., Iwabuchi, H.: A new method of measuring aerosol optical properties from digital twilight photographs. Atmos. Meas. Tech. 8(10), 4295–4311 (2015)CrossRefGoogle Scholar
  31. 31.
    Tang, M., Agrawal, P., Pongpaichet, S., Jain, R.: Geospatial interpolation analytics for data streams in eventshop. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2015)Google Scholar
  32. 32.
  33. 33.
    Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013)CrossRefGoogle Scholar
  34. 34.
  35. 35.
    XML Path Language (XPath).
  36. 36.
    Zerefos, C., Tetsis, P., Kazantzidis, A., Amiridis, V., Zerefos, S., Luterbacher, J., Eleftheratos, K., Gerasopoulos, E., Kazadzis, S., Papayannis, A.: Further evidence of important environmental information content in red-to-green ratios as depicted in paintings by great masters. Atmos. Chem. Phys. 14(6), 2987–3015 (2014)CrossRefGoogle Scholar
  37. 37.
    Zheng, H.T., Kang, B.Y., Kim, H.G.: An ontology-based approach to learnable focused crawling. Inf. Sci. 178(23), 4512–4522 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Anastasia Moumtzidou
    • 1
    Email author
  • Symeon Papadopoulos
    • 1
  • Stefanos Vrochidis
    • 1
  • Ioannis Kompatsiaris
    • 1
  • Konstantinos Kourtidis
    • 2
  • George Hloupis
    • 3
  • Ilias Stavrakas
    • 3
  • Konstantina Papachristopoulou
    • 4
  • Christodoulos Keratidis
    • 4
  1. 1.Centre for Research and Technology Hellas - Information Technologies InstituteThessalonikiGreece
  2. 2.Democritus University of ThraceXanthiGreece
  3. 3.Technological Education Institute of AthensAthensGreece
  4. 4.DRAXIS Environmental Technologies CompanyThessalonikiGreece

Personalised recommendations