CfP: MACLEAN: MAChine Learning for EArth ObservatioN (workshop @ECML/PKDD2019)
Roberto Interdonato
Dino Ienco
Dino Ienco
================================================================================================================================================
Website: https://mdl4eo.irstea.fr/maclean-machine-learning-for-earth-observation/
KEY DATES
Paper submission deadline: June 10th, 2019
Rejected Conference Papers sent to Workshops: July 5th 2019
Paper acceptance notification: July 19th, 2019
Paper camera-ready deadline: Monday, July 26th, 2019
Workshop date: Friday, September 20th, 2019 (to be confirmed)
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, colocated with the ECML/PKDD2019 conference, 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.
Sébastien Lefèvre, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
Program Committee
Xiaowei Jia, University of Minnesota
Devis Tuia, Wageningen University and Research
Giuseppe Scarpa, University of Naples Federico II, Italy
Marc Russwurm, Munchen University
Raffaele Gaetano, CIRAD
Jonathan Weber, Université de Haute-Alsace, France
Germain Forestier, Université de Haute-Alsace, France
Indré Zliobaite, University of Helsinki, Finland
François Petit-Jean, Monash University, Australia
Camille Kurtz, Université Paris Descartes, France
Charlotte Pelletier, Monash University, Australia
Begüm Demir, Technische Universität Berlin, Germany
Romain Tavenard, Université Rennes 2, France
Nicolas Courty, Université Bretagne Sud, France
Pedram Ghamisi, Dresden University, Germany
Jan Wegner, ETH Zurich, Swiss
Alexandre Boulch, ONERA, France
Ribana Roscher, University of Bonn, Germany
Xiaoxiang Zhu, TU Munich / DLR, Germany
Yuliya Tarabalka, LuxCarta / INRIA, France
Nicolas Audebert, CEA, France
Mihai Datcu, DLR, Germany
CONTACT
================================================================================================================================================
Dino Ienco, PhD, HDR
Irstea Researcher
UMR TETIS, Montpellier
500, rue Jean-François Breton,
F-34093 Montpellier, France
Tel: +33 (0)4.67.55.86.07
e-mail: dino.ienco@irstea.fr
web: https://sites.google.com/site/dinoienco/
Pour mieux affirmer ses missions,
le Cemagref devient Irstea
Dino Ienco
Dino Ienco
Website: https://mdl4eo.irstea.fr/maclean-machine-learning-for-earth-observation/
Workshop colocated with ECML/PKDD 2019
KEY DATES
Paper submission deadline: June 10th, 2019
Rejected Conference Papers sent to Workshops: July 5th 2019
Paper acceptance notification: July 19th, 2019
Paper camera-ready deadline: Monday, July 26th, 2019
Workshop date: Friday, September 20th, 2019 (to be confirmed)
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, colocated with the ECML/PKDD2019 conference, 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
Clustering 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
Bayesian machine learning for EO Data
Dimensionality Reduction and Feature Selection for EO Data
Graphical 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
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 2019 submission format.
Accepted contributions will be made available electronically through the Workshop web page.
Post-proceedings will be also published in LNCSI and have them included in the series Lecture Notes in Computer Science (LNCS).
SUBMISSION WEBSITE:
https://easychair.org/conferences/?conf=maclean2019
PC-CHAIRS:
Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France
Dino Ienco, IRSTEA, UMR Tetis, Montpellier, France
Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France
Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
Sébastien Lefèvre, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
Dino Ienco
