=== 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.


=== Important Dates ===

- 10 October 2020 - workshop submission deadline
- 10 November 2020 - author notification
- 15 November 2020 - camera-ready submission
- 1 December 2020 - finalized workshop program


=== Organizers ===

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


== Publicity chair ==

Fabio Bellavia, Universita' di Palermo, Italy

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

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