Data Science Chair

    Environmental Science

    The DMIR group works with environmental data to contribute to the ongoing climate research. Based on low-cost sensors, map and geographical data, we develop novel approaches to environmental questions. These questions include "What is the air pollution at a given spot on earth?" and "What type of wine should I grow on my land given the development in climate in the next few decades?". While answering such questions is one concern of our research, the other objective is to raise awareness for the environment in the society.


    We are currently working on the following projects:


    Combining low-cost mobile sensors and subjective feedback to build understandable maps regarding air pollution in Würzburg.

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    Developing machine learning based approaches to regional environmental prediction models and answering future-related questions by small and medium-sized enterprises regarding environmental factors.


    Here is a list of selected publications.

    • Participatory sensing, op... - Download
      Participatory sensing, opinions and collective awareness Loreto, Vittorio; Haklay, Mordechai; Hotho, Andreas; Servedio, Vito C. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca (2017). Springer.
    • Collective Sensing Platfo... - Download
      Collective Sensing Platforms. Atzmueller, Martin; Becker, Martin; Mueller, Juergen in Participatory Sensing, Opinions and Collective Awareness (2017). 115--133.
    • Participatory Patterns in... - Download
      Participatory Patterns in an International Air Quality Monitoring Initiative. Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris in PLoS ONE (2015). 10(8) e0136763.
    • Awareness and learning in... - Download
      Awareness and learning in participatory noise sensing. Becker, Martin; Caminiti, Saverio; Fiorella, Donato; Francis, Louise; Gravino, Pietro; Haklay, Mordechai (Muki); Hotho, Andreas; Loreto, Vittorio; Mueller, Juergen; Ricchiuti, Ferdinando; Servedio, Vito D. P.; Sirbu, Alina; Tria, Francesca in PLOS ONE (2013). 8(12) e81638.