Connectionists: CfP: MACLEAN - MAChine Learning for EArth ObservatioN (workshop @ECML/PKDD2024)

[Roberto Interdonato]

MACLEAN: MAChine Learning for EArth ObservatioN September 2024 KEY DATES
Paper submission deadline: June 15, 2024 Paper acceptance notification:
July 15, 2024 Paper camera-ready deadline: July 30, 2024

CONTEXT The huge amount of data currently produced by modern Earth
Observation (EO) missions has raised up new challenges for the Remote
Sensing communities. EO sensors are now able to offer (very) high spatial
resolution images with revisit time frequencies never achieved before
considering different kind of signals, e.g., multi-(hyper)spectral optical,
radar, LiDAR and Digital Surface Models. In this context, modern machine
learning techniques can play a crucial role to deal with such amount of
heterogeneous, multi-scale and multi-modal data. Some examples of
techniques that are gaining attention in this domain include deep learning,
domain adaptation, semi-supervised approach, time series analysis and
active learning. Even though the use of machine learning and the
development of ad-hoc techniques are gaining increasing popularity in the
EO domain, we can witness that a significant lack of interaction between
domain experts and machine learning researchers still exists. The objective
of this workshop is to supply an international forum where machine learning
researchers and domain-experts can meet each other, in order to exchange,
debate and draw short and long term research objectives around the
exploitation and analysis of EO data via Machine Learning techniques. Among
the workshop’s objectives, we want to give an overview of the current
machine learning researches dealing with EO data, and, 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.

TOPICS – Supervised Classification of Multi(Hyper)-spectral data –
Supervised Classification of Satellite Image Time Series data –
Unsupervised of EO Data – Deep Learning approaches to deal with EO Data –
Machine Learning approaches for the analysis of multi-scale EO Data –
Machine Learning approaches for the analysis of multi-source EO Data –
Semi-supervised classification approaches for EO Data – Active learning for
EO Data – Transfer Learning and Domain Adaptation for EO Data
Interpretability and explainabiilty of machine learning methods in the
context of EO data analysis – Bayesian machine learning for EO Data –
Dimensionality Reduction and Feature Selection for EO Data – Graphicals
models for EO Data – Structured output learning for EO Data – Multiple
instance learning for EO Data – Multi-task learning for EO Data – Online
learning for EO Data – Embedding and Latent factor for EO Data


INVITED SPEAKERS:

- Matthieu Molinier, VTT Technical Research Centre of Finland, Finland

Keynote Title: Improving temporal frequency of dense multispectral image
time series : applications for environmental monitoring


- Giuseppina Andresini & Annalisa Appice, Università di Bari, Italy
Keynote Title: "Monitoring forest health with AI: approaches for mapping
tree dieback in satellite data"

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
2024 submission format. Accepted contributions will be made available
electronically through the Workshop web page. Post-proceedings will be also
published at the CCIS (Communications in Computer and Information Science)
series.

WORKSHOP WEBSITE: https://sites.google.com/view/maclean24 SUBMISSION
WEBSITE:
https://cmt3.research.microsoft.com/ECMLPKDDWorkshops2024/Track/7/Submission/Create

PC-CHAIRS Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes,
France, thomas.corpetti at cnrs.fr Roberto Interdonato, CIRAD, UMR Tetis,
Montpellier, France, roberto.interdonato at cirad.fr Cassio Fraga Dantas,
INRAE, UMR Tetis, Montpellier, France, cassio.fraga-dantas at inrae.fr Dino
Ienco, INRAE, UMR Tetis, Montpellier, France, dino.ienco at inrae.fr Minh-Tan
Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France,
minh-tan.pham at irisa.fr
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