Connectionists: Call for Papers - ICPR2020 workshop on "Machine Learning Advances Environmental Science (MAES)"

[Antonino Staiano]

                  MAES2020 workshop at ICPR2020
      *** UPDATES in relation to COVID-19 (Coronavirus) ***

        ---===== Apologies for multiple posting =====---
        Please distribute this call to interested parties
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 Machine Learning Advances Environmental Science (MAES at ICPR2020)

                         workshop at the
 25th International Conference on Pattern Recognition (ICPR2020)
             Milan, Italy, January 10-15, 2021

     >>> https://sites.google.com/view/maes-icpr2020/ <<<

__________________________________________________________________

=== UPDATES in relation to COVID-19 (Coronavirus) ===

The MAES2020 workshop as well as the ICPR2020 conference have been
postponed to January 10-15, 2021!

More updates and details are available at

https://www.micc.unifi.it/icpr2020/index.php/2020/03/03/update-icpr2020-covid19/


 === New Important Dates ===

- Workshop submission deadline: October 10th

- Workshop author notification:  November 10th

- Camera-ready submission: November 15th

- Finalized workshop program: December 1st


=== Aim & Scope ===

Environmental data are growing steadily in volume, complexity, and
diversity to Big Data mainly driven by advanced sensor technology. Machine
learning can offer superior techniques for unraveling complexity, knowledge
discovery and predictability of Big Data environmental science.

The aim of the workshop is to provide a state-of-the-art survey of
environmental research topics that can benefit from Machine Learning
methods and techniques. To this purpose, the workshop welcomes papers on
successful environmental applications of machine learning and pattern
recognition techniques to diverse domains of Environmental Research, for
instance, recognition of biodiversity in thermal, photo and acoustic
images, natural hazards analysis and prediction, environmental remote
sensing, estimation of environmental risks, prediction of the
concentrations of pollutants in geographical areas, environmental threshold
analysis and predictive modelling,  estimation of Genetical Modified
Organisms (GMO) effects on non-target species.

The workshop will be the place to make an analysis of the advances of
Machine Learning for Environmental Science and should indicate the open
problems in environmental research that still have not properly benefited
from Machine Learning.

Extended papers of this workshop will be published as a special issue in
the journal of Environmental Modelling and Software, Elsevier.


 === Organizers ===

  Francesco Camastra, Universita' degli Studi di Napoli Parthenope, Italy
 Friedrich Recknagel, University of Adelaide, Australia
    Antonino Staiano, Universita' degli Studi di Napoli Parthenope, Italy

_________________________________________________________

 Contacts: antonino.staiano at uniparthenope.it
           francesco.camastra at uniparthenope.it

 Workshop: https://sites.google.com/view/maes-icpr2020/
 ICPR2020: https://www.micc.unifi.it/icpr2020/






Antonino Staiano, PhD
Dept. Science and Technology
University of Naples Parthenope, Italy
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