Towards Air Quality Estimation Using Collected Multimodal Environmental Data

TL;DR

This paper introduces an open platform that integrates multimodal environmental data from various sources to improve air quality estimation with higher coverage and granularity.

Contribution

It presents a novel open platform for collecting and fusing diverse environmental data sources to enhance air quality monitoring capabilities.

Findings

Increased geographical coverage of air quality data.

Enhanced temporal granularity through data fusion.

Utilization of recent advances in image analysis and machine learning.

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 and is expected to result in increased geographical coverage and temporal granularity of air quality data.