Machine Learning Advances Environmental Science Call for Papers

 Call for Papers - M A E S @ICPR2020

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


   Machine Learning Advances Environmental Science (MAES@ICPR2020)

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

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 === Important Dates ===

   June 15th 2020 - workshop submission deadline
   July 15th 2020 - author notification
   July 30th 2020 - camera-ready submission
 August 15th 2020 - finalized workshop program

 === 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
unravelling 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 the 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