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 and is expected to result in increased geographical coverage and temporal granularity of air quality data.
@article{arxiv.1610.01209,
title = {Towards Air Quality Estimation Using Collected Multimodal Environmental Data},
author = {Anastasia Moumtzidou and Symeon Papadopoulos and Stefanos Vrochidis and Ioannis Kompatsiaris and Konstantinos Kourtidis and George Hloupis and Ilias Stavrakas and Konstantina Papachristopoulou and Christodoulos Keratidis},
journal= {arXiv preprint arXiv:1610.01209},
year = {2016}
}