Connectionists: CfP: MACLEAN - MAChine Learning for EArth ObservatioN (workshop @ECML/PKDD2023)

[Roberto Interdonato]

*MACLEAN: MAChine Learning for EArth ObservatioN*

September 2023 (18 or 22)

https://sites.google.com/view/maclean23/





*KEY DATES*



Paper submission deadline: *June 12, 2023*

Paper acceptance notification: *July 12, 2023*



*CONTEXT*

The vast amount of data currently produced by modern Earth Observation (EO)
missions and measurements on the surface has raised new challenges for the
Remote Sensing Community and atmospheric modelers. EO sensors can now offer
(very) high spatial resolution images with revisit time frequencies never
achieved before considering different signals, e.g., multi-(hyper)spectral
optical, radar, LiDAR, and Digital Surface Models.

On the other hand, atmospheric composition and processes are measured on
the surface, starting from molecular scale measurements with mass
spectrometers, particle counters, and more traditional meteorological
instruments. Modern machine learning techniques can be crucial in dealing
with such heterogeneous, multi-scale, and multi-modal data.

Some methods gaining attention in this domain include deep learning, domain
adaptation, semi-supervised approach, time series analysis, active
learning, explainable artificial intelligence, uncertainty quantification,
and interactive model building and visualization. Even though machine
learning and the development of ad-hoc techniques are gaining popularity,
we still see a significant need for more interaction between domain experts
and machine learning researchers.

This workshop aims to be an international forum where machine learning
researchers and domain experts can meet each other to exchange, debate, and
draw short and long-term research objectives around the exploitation and
analysis of EO and atmospheric data via Machine Learning techniques. Among
the workshop’s goals, we want to give an overview of the current
machine-learning research dealing with EO and other atmospheric measurement
data. On the other hand, we want to stimulate concrete discussions to pave
the way to new machine learning frameworks especially tailored to deal with
such data.


*INVITED SPEAKERS*
* Gabriele Moser*, University of Genoa

*Jonas Elm*, Aarhus University



*TOPICS*



The non-exclusive list of topics for the workshop includes, to the extent
related to the EO and atmospheric procesess:

·       Supervised and unsupervised machine learning methods

·       Semi-supervised classification, domain adoptation, active learning,
structured output learning, multi-task learning, and online learning

·       Interpretability and explainability of machine learning methods

·       Bayesian modelling of various parts of EO or atmospheric procesess

·       Dimensionality reduction and feature selection, finding embeddings
and latent variables

·       Visualisation and interaction with EO and atmospheric data

·       Interactive model building and eliciting expert knowledge

·       Applications of high-performance computing



*SUBMISSION*



We welcome original contributions, either theoretical or empirical,
describing ongoing

projects or completed work. Contributions can be of two types: either short
position papers (up to 6 pages including references) or full research
papers (up to 10 pages including references). Papers must be written in
LNCS format, i.e., accordingly to the ECML-PKDD 2023 submission format.
Accepted contributions will be made available electronically through the
Workshop web page. Post-proceedings will be published by Springer.



*WORKSHOP WEBSITE:*



https://sites.google.com/view/maclean23/



*SUBMISSION WEBSITE:*



https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Submission/Index


*Please select MACLEAN from the drop down menu when creating your
submission.*



*PC-CHAIRS*



Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France,

thomas.corp <thomas.corpetti at cnrs.fr>etti at cnrs.fr

Dino Ienco, INRAE, UMR Tetis, Montpellier, France, di <dino.ienco at inrae.fr>
no.ienco at inrae.fr

Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France, roberto.in
<roberto.interdonato at cirad.fr>terdonato at cirad.fr

Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France,
minh-tan.pham at irisa.fr

Patrick Rinke, Aalto University, Helsinki, patrick.rinke at aalto.fi

Kai Puolamäki, University of Helsinki, Helsinki, Finland,
kai.puolamaki at helsinki.fi
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