MACLEAN: MAChine Learning for EArth ObservatioN (workshop @ECML/PKDD2019)


Paper submission deadline: June 10th, 2019 June 17th, 2019

Rejected Conference Papers sent to Workshops: July 5th 2019
Paper acceptance notification: July 19th, 2019
Paper camera-ready deadline: Friday, July 26th, 2019
Workshop date: Friday, Septembre 20th, 2019 (to be confirmed)


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.


10h30 – 10h40 Opening
10h40 – 11h40 Invited Speaker: Prof. Robert Jenssen
Deep learning for power line inspection

11h40 – 12h40 1st Session (3 papers): “Prediction Tasks on Earth Observation Data”
– G. Okhotnikov and N. Golyandina: EOP Time Series Prediction Using Singular Spectrum Analysis
– M. Izbicki, E. Papalexakis and V. Tsotras: The MvMF Loss for Predicting Locations on the Earth’s Surface
– C.-Y. Bai, B.-F. Chen and H.-T. Lin: Attention-based Deep Tropical Cyclone Rapid Intensification Prediction

14h00 – 15h00 Invited Speaker: Dr. Xiaoxiang Zhu
AI4EO: Artificial Intelligence in Earth Observation

15h00 – 16h00 2nd Session (3 papers): “Satellite Image Time Series Analysis”
– J. E. Gbodjo, L. Leroux, R. Gaetano and B. Ndao: RNN-based Multi-Source Land Cover mapping: An application to West African landscape
– B. Lafabregue, J. Weber, P. Gançarski and G. Forestier: Deep constrained clustering applied to remote sensing time series
– K. Reis Ferreira, L. Alves Santos and M. C. A. Picoli: Evaluating distance measures for image time series clustering in land use and cover monitoring

16h00 – 16h20 Coffee Break

16h20 – 17h00 3rd Session (2 papers): “Change Detection”
– A. Appice, N. Di Mauro, F. Lomuscio and D. Malerba: Empowering Change Vector Analysis with Autoencoding in Bi-temporal Hyperspectral Images
– A. Farasin, G. Nini, P. Garza and C. Rossi: Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery

17h00 – 17h15 Closing


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
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


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).



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

Program Committee

Xiaowei Jia, University of Minnesota
Devis Tuia, Wageningen University and Research
Giuseppe Scarpa, University of Naples Federico II, Italy
Marc Rußwurm, Technical University of Munich
Raffaele Gaetano, CIRAD
Jonathan Weber, Université de Haute-Alsace, France
Germain Forestier, Université de Haute-Alsace, France
Indré Zliobaite, University of Helsinki, Finland
François PetitJean, 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, Quicksign, France
Mihai Datcu, DLR, Germany


Sponsored by GDR MADICS