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....@cnrs.fr Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France, roberto.i...@cirad.fr Cassio Fraga Dantas, INRAE, UMR Tetis, Montpellier, France, cassio.fr...@inrae.fr Dino Ienco, INRAE, UMR Tetis, Montpellier, France, dino....@inrae.fr Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France, minh-t...@irisa.fr
Roberto Interdonato
Roberto Interdonato
DEADLINE EXTENDED! Full Paper due June 24
MACLEAN: MAChine Learning for EArth ObservatioN September 2024
KEY DATES
Paper submission deadline: June 24, 2024