CfPs: 2nd Edition MACLEAN (Machine Learning for Earth Observation

CfPs: 2nd Edition MACLEAN (Machine Learning for Earth Observation


Workshop colocated with ECML/PKDD 2020

Paper submission deadline: June 9th, 2020
Rejected Conference Papers sent to Workshops: June 9th, 2020
Paper acceptance notification: July 9th, 2020
Paper camera-ready deadline: TBA
Workshop date: September 14 or September 18 (TBA)

The huge amount of data currently produced by modern Earth Observation
(EO) missions has raised up new challenges for the Remote Sensing
communities. EO sen- sors 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 addition, con-
sidering successive acquisitions of satellite imagery over the same
area, it is also possible to organize these data as satellite image time
series (SITS), in order to track and monitor phenomena over time.
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 learn- ing, domain adaptation,
semi-supervised approach, time series analysis and active learning.
Leveraging Machine Learning approaches for the analysis of EO data can
be beneficial (or even crucial) for several EO-related tasks such as
precision agri- culture and food risk prevention, mapping biodiversity,
monitoring climate changes, understanding temporal trajectories related
to the evolution of natural habitats and generally, manage resources in
a territory and provide more accurate information on environmental and
anthropic phenomena.
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 re- searchers still exists. People coming from
these two different communities, currently, may often have some
difficulties in structuring themselves around these issues due to a lack
of mutual knowledge.
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 objec- tives, 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.

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

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).