Mining atmospheric data

TL;DR

This paper discusses creating public datasets and applying deep learning to classify atmospheric data for air quality prediction, aiming for fast, reliable monitoring at local and regional scales.

Contribution

It introduces new datasets and benchmarks for remote sensing data and explores deep learning methods for atmospheric data classification with limited annotations.

Findings

Development of public datasets for atmospheric monitoring

Deep learning models for air quality prediction

Potential for real-time, regional air quality assessment

Abstract

This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates the building new public datasets and benchmarks, which are hot priority of the remote sensing community. The second issue is the investigation of deep learning methodologies for atmospheric data classification based on vast amount of data without annotations and with localized annotated data provided by sparse observing networks at the surface. The targeted application is air quality assessment and prediction. Air quality is defined as the pollution level linked with several atmospheric constituents such as gases and aerosols. There are dependency relationships between the bad air quality, caused by air pollution, and the public health. The target application is the development of a fast prediction model for local and regional air quality assessment and tracking. The results of mining data will have significant implication for citizen and decision makers by providing a fast prediction and reliable air quality monitoring system able to cover the local and regional scale through intelligent extrapolation of sparse ground-based in situ measurement networks.