<div dir="ltr"><p class="MsoNormal" style="margin:0cm 0cm 0cm 1pt;line-height:10.65pt;font-size:11pt;font-family:"Times New Roman",serif"><b><span lang="EN-US" style="font-size:10pt">MACLEAN: MAChine Learning for EArth<span style="letter-spacing:2.6pt"> </span>ObservatioN</span></b><b><span lang="EN-US" style="font-size:10pt"></span></b></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">September 2023 (18 or 22)</span><span lang="EN-US"></span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><a href="https://sites.google.com/view/maclean23/" target="_blank" style="color:rgb(5,99,193)">https://sites.google.com/view/maclean23/</a></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>KEY DATES</u></span><span lang="EN-US"></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Paper submission deadline: <b>June 12, 2023</b></span><span lang="EN-US"></span></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Paper<span style="letter-spacing:1.25pt"> </span>acceptance<span style="letter-spacing:1.25pt"> </span>notification:<span style="letter-spacing:1.25pt"> </span><b>July<span style="letter-spacing:1.25pt"> </span>12,<span style="letter-spacing:1.25pt"> </span>2023</b></span><span lang="EN-US"></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>CONTEXT</u></span><span lang="EN-US"></span></p><p style="margin-left:0cm;text-align:justify;margin-right:0cm;margin-bottom:0cm;font-size:10pt;font-family:"Times New Roman",serif;color:black"><span lang="EN-US" style="font-size:11pt">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.</span></p><p style="margin-left:0cm;text-align:justify;margin-right:0cm;margin-bottom:0cm;font-size:10pt;font-family:"Times New Roman",serif;color:black"><span lang="EN-US" style="font-size:11pt">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.</span></p><p style="margin-left:0cm;text-align:justify;margin-right:0cm;margin-bottom:0cm;font-size:10pt;font-family:"Times New Roman",serif;color:black"><span lang="EN-US" style="font-size:11pt">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.</span></p><p style="margin-left:0cm;text-align:justify;margin-right:0cm;margin-bottom:0cm;font-size:10pt;font-family:"Times New Roman",serif;color:black"><span lang="EN-US" style="font-size:11pt">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.<br><br><u>INVITED SPEAKERS<br></u><span style="font-size:11pt"><br><b>
Gabriele Moser</b>, University of Genoa</span><br></span></p><p style="margin-left:0cm;text-align:justify;margin-right:0cm;margin-bottom:0cm;font-size:10pt;font-family:"Times New Roman",serif;color:black"><span lang="EN-US" style="font-size:11pt"></span></p><p class="MsoNormal"><span style="font-size:12pt;font-family:"Times New Roman",serif" lang="EN-GB"><b>Jonas Elm</b>, Aarhus University</span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>TOPICS</u></span></p><p style="margin:0cm;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-GB">The non-exclusive list of topics for the workshop includes, to the extent related to the EO and atmospheric procesess:</span><span lang="EN-US"> </span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Supervised and unsupervised machine learning methods</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Semi-supervised classification, domain adoptation, active learning, structured output learning, multi-task learning, and online learning</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Interpretability and explainability of machine learning methods</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Bayesian modelling of various parts of EO or atmospheric procesess</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Dimensionality reduction and feature selection, finding embeddings and latent variables</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Visualisation and interaction with EO and atmospheric data</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Interactive model building and eliciting expert knowledge</span></p><p style="margin:0cm 0cm 0cm 36pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-family:Symbol">·<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">       </span></span><span lang="EN-US">Applications of high-performance computing</span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>SUBMISSION</u></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="text-align:justify;line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">We welcome original contributions, either theoretical or empirical, describing ongoing</span><span lang="EN-US"></span></p><p style="margin:0.45pt 0.85pt 0.0001pt 1pt;text-align:justify;line-height:13.6px;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">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.</span><span lang="EN-US"></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>WORKSHOP<span style="letter-spacing:-1.15pt"> </span>WEBSITE:</u></span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><a href="https://sites.google.com/view/maclean23/" target="_blank" style="color:rgb(5,99,193)">https://sites.google.com/view/maclean23/</a></span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>SUBMISSION WEBSITE:</u></span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><a href="https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Submission/Index">https://cmt3.research.microsoft.com/ECMLPKDDworkshop2023/Submission/Index</a></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><br></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><b>Please select MACLEAN from the drop down menu when creating your submission.</b></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"> <br></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><u>PC-CHAIRS</u></span><span lang="EN-US"></span></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US"> </span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France,</span><span lang="EN-US"></span></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US"><a href="mailto:thomas.corpetti@cnrs.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">thomas.corp</span></a><a href="mailto:etti@cnrs.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">etti@cnrs.fr</span></a></span></p><p style="margin:0.45pt 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Dino Ienco, INRAE, UMR Tetis, Montpellier, France, </span><span lang="EN-US"><a href="mailto:dino.ienco@inrae.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">di</span></a><a href="mailto:no.ienco@inrae.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">no.ienco@inrae.fr</span></a></span></p><p style="margin:0.45pt 0.85pt 0.0001pt 1pt;line-height:13.6px;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France, </span><span lang="EN-US"><a href="mailto:roberto.interdonato@cirad.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">roberto.in</span></a><a href="mailto:terdonato@cirad.fr" target="_blank"><span style="color:windowtext;text-decoration-line:none">terdonato@cirad.fr</span></a></span></p><p style="margin:0.45pt 0.85pt 0.0001pt 1pt;line-height:13.6px;font-size:10pt;font-family:"Times New Roman",serif">Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France, <a href="mailto:minh-tan.pham@irisa.fr" target="_blank">minh-tan.pham@irisa.fr</a></p><p style="margin:0.45pt 0.85pt 0.0001pt 1pt;line-height:13.6px;font-size:10pt;font-family:"Times New Roman",serif"><span lang="EN-US">Patrick Rinke, Aalto University, Helsinki, <a href="mailto:patrick.rinke@aalto.fi" target="_blank">patrick.rinke@aalto.fi</a></span><span lang="EN-US"></span></p><p style="line-height:10.65pt;margin:0cm 0cm 0cm 1pt;font-size:10pt;font-family:"Times New Roman",serif"></p><p class="MsoNormal" style="margin:0cm;font-size:11pt;font-family:"Times New Roman",serif"><span lang="EN-US" style="font-size:10pt">Kai Puolamäki, University of Helsinki, Helsinki, Finland, <a href="mailto:kai.puolamaki@helsinki.fi" target="_blank">kai.puolamaki@helsinki.fi</a></span></p><div></div></div>