Mining atmospheric data
This paper overviews two interdependent issues important for mining remote sensing data obtained from atmospheric monitoring missions to investigate deep learning methodologies for atmospheric data classification based on vast amount of data and without ground truth or with very limited ground truth.
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 to building new public datasets and benchmarks, which are hot priority of the remote sensing community. The second issue is to investigate deep learning methodologies for atmospheric data classification based on vast amount of data and without ground truth or with very limited ground truth. The targeted application is air quality assessment and prediction. Air quality is defined as the pollution level linked with several atmospheric constituent such as gases and aerosols. Low levels of air quality and thus high levels of air pollution led to increase in public health issues. 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.