  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.
