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<font size="4"><b>MACLEAN: MAChine Learning for EArth ObservatioN</b></font></p>
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<b>Conference dates:</b> September, 7-11 2026</p>
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<b>Location:</b> Naples, Italy</p>
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<b>Best paper prize sponsored by ESA </b></p>
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<b>KEY DATES</b></p>
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<ul style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">
<li><b>Paper submission deadline:</b> June 14, 2026<br>
</li><li><b>Paper acceptance notification</b>: July 14, 2026<br>
</li><li><b>Paper camera-ready deadline:</b> July 30, 2026<br>
</li></ul>
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<b>CONTEXT</b></p>
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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. </p>
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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.</p>
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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.</p>
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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.</p>
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<b>TOPICS</b></p>
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<li><span style="font-size: 13px;">Supervised Classification of Multi(Hyper)-spectral data</span><br>
</li><li><span style="font-size: 13px;">Supervised Classification of Satellite Image Time Series data</span><br>
</li><li><span style="font-size: 13px;">Unsupervised Learning of EO Data</span><br>
</li><li><span style="font-size: 13px;">Deep Learning approaches to deal with EO Data</span><br>
</li><li><span style="font-size: 13px;">Machine Learning approaches for the analysis of multi-scale EO Data</span><br>
</li><li><span style="font-size: 13px;">Machine Learning approaches for the analysis of multi-source EO Data</span><br>
</li><li><span style="font-size: 13px;">Semi-supervised classification approaches for EO Data</span><br>
</li><li><span style="font-size: 13px;">Active learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Transfer Learning and Domain Adaptation for EO Data</span><br>
</li><li><span style="font-size: 13px;">Interpretability and explainability of machine learning methods in the context of EO data analysis</span><br>
</li><li><span style="font-size: 13px;">Bayesian machine learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Dimensionality Reduction and Feature Selection for EO Data</span><br>
</li><li><span style="font-size: 13px;">Graphicals models for EO Data</span><span style="font-size: 13px;"> </span><br>
</li><li><span style="font-size: 13px;">Structured output learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Multiple instance learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Multi-task learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Online learning for EO Data</span><br>
</li><li><span style="font-size: 13px;">Embedding and Latent factor for EO Data</span><br>
</li><li><span style="font-size: 13px;">Foundation Models for Earth Observation</span><br>
</li><li><span style="font-size: 13px;">Multi-Modal approaches for EO Data</span><span style="font-size: 13px;"> </span><br>
</li><li><span style="font-size: 13px;">Self-supervised learning for EO Data</span></li></ul>
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<b>INVITED SPEAKERS: </b></p>
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<ul style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">
<li><b>Dr. Valerio Marsocci - </b>Research fellow at ESA PhiLab<br>
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<b>SUBMISSION</b></p>
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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.</p>
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Post-proceedings will be also published at the CCIS (Communications in Computer and Information Science) series.</p>
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<b>WORKSHOP WEBSITE:</b></p>
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<span style="text-decoration: underline;"><a href="https://sites.google.com/view/maclean26" style="text-decoration: none; color: rgb(41, 98, 255);">https://sites.google.com/view/maclean26</a></span></p>
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<b>SUBMISSION WEBSITE: </b></p>
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<a href="https://cmt3.research.microsoft.com/ECMLPKDDWT2026/Track/10/Submission/Create">https://cmt3.research.microsoft.com/ECMLPKDDWT2026/Track/10/Submission/Create</a></p>
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<b>PC-CHAIRS</b></p>
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<ul>
<li><span style="font-size: 13px;">Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France, </span><a href="mailto:thomas.corpetti@cnrs.fr" style="text-decoration: none; color: rgb(41, 98, 255); font-size: 13px;"><span style="text-decoration: underline;">thomas.corpetti@cnrs.fr</span></a><br>
</li><li><span style="font-size: 13px;">Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France, </span><a href="mailto:roberto.interdonato@cirad.fr" style="text-decoration: none; color: rgb(41, 98, 255); font-size: 13px;"><span style="text-decoration: underline;">roberto.interdonato@cirad.fr</span></a><br>
</li><li><span style="font-size: 13px;">Cássio Fraga Dantas, INRAE, UMR Tetis, Montpellier, France, </span><a href="mailto:cassio.fraga-dantas@inrae.fr" style="text-decoration: none; color: rgb(41, 98, 255); font-size: 13px;"><span style="text-decoration: underline;">cassio.fraga-dantas@inrae.fr</span></a><br>
</li><li><span style="font-size: 13px;">Giuseppe Guarino, INRAE, UMR Tetis, Montpellier, France, </span><a href="mailto:giuseppe.guarino@inrae.fr" style="text-decoration: none; color: rgb(41, 98, 255); font-size: 13px;"><span style="text-decoration: underline;">giuseppe.guarino@inrae.fr</span></a><br>
</li><li><span style="font-size: 13px;">Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France, </span><span style="font-size: 13px; text-decoration: underline;"><a href="mailto:minh-tan.pham@irisa.fr" style="text-decoration: none; color: rgb(41, 98, 255);">minh-tan.pham@irisa.fr</a></span></li></ul>
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