Towards air quality estimation using collected multimodal environmental data

Abstract

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.