Date : 2026-09-07
Lieu : Naples, Italy
MACLEAN: MAChine Learning for EArth ObservatioN
Conference dates: September, 7-11 2026
Location: Naples, Italy
Best paper prize sponsored by ESA
KEY DATES
Paper submission deadline: June 14, 2026
Paper acceptance notification: July 14, 2026
Paper camera-ready deadline: July 30, 2026
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 Learning 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 explainability 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
Foundation Models for Earth Observation
Multi-Modal approaches for EO Data
Self-supervised learning for EO Data
INVITED SPEAKERS:
Dr. Valerio Marsocci – Research fellow at ESA PhiLab
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 2026 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/maclean26
SUBMISSION WEBSITE:
https://cmt3.research.microsoft.com/ECMLPKDDWT2026/Track/10/Submission/Create
PC-CHAIRS
Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France, thomas.corpetti@cnrs.fr
Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France, roberto.interdonato@cirad.fr
Cássio Fraga Dantas, INRAE, UMR Tetis, Montpellier, France, cassio.fraga-dantas@inrae.fr
Giuseppe Guarino, INRAE, UMR Tetis, Montpellier, France, giuseppe.guarino@inrae.fr
Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France, minh-tan.pham@irisa.fr
Notre site web : www.madics.fr
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