Sensing the Air We Breathe — The OpenSense Zurich Dataset

Authors

  • Jason Jingshi Li EPFL
  • Boi Faltings EPFL
  • Olga Saukh ETHZ
  • David Hasenfratz ETHZ
  • Jan Beutel ETHZ

DOI:

https://doi.org/10.1609/aaai.v26i1.8163

Keywords:

air pollution, spatio-temporal modeling, machine learning, spatial constraints

Abstract

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem,introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.

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Published

2021-09-20

How to Cite

Li, J. J., Faltings, B., Saukh, O., Hasenfratz, D., & Beutel, J. (2021). Sensing the Air We Breathe — The OpenSense Zurich Dataset. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 323-325. https://doi.org/10.1609/aaai.v26i1.8163

Issue

Vol. 26 No. 1 (2012): Twenty-Sixth AAAI Conference on Artificial Intelligence

Section

AAAI Technical Track: Computational Sustainability