Workshop on Machine Learning for Earth Observation
In Conjunction with the ECML/PKDD 2022
Grenoble, September 19, 2022
Aims and Scopes:
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.
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
The workshop will be held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) which will be held in Grenoble from the 19th to the 23rd of September 2022 (official website : https://2022.ecmlpkdd.org/).