GUEST EDITORS (Listed alphabetically):
* Thomas Corpetti, CNRS, LETG-Rennes COSTEL UMR 6554 CNRS, Rennes, France
* Dino Ienco, INRAE, UMR Tetis, Montpellier, France
* Roberto Interdonato, CIRAD, UMR Tetis, Montpellier, France
* Sébastien Lefèvre, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
* Minh-Tan Pham, Univ. Bretagne-Sud, UMR 6074, IRISA, Vannes, France
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, by means of a large family of signals (i.e. multi-(hyper)spectral optical, radar, LiDAR, Digital Surface Models, etc..).
In this context, modern machine learning techniques (i.e. deep learning, domain adaptation, semi-supervised approach, time series analysis, active learning) can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. For example, leveraging Machine Learning approaches for the analysis of EO data is crucial for precision agriculture and food risk prevention, mapping biodiversity, monitoring climate changes, understanding temporal trajectories for the evolution of natural habitats, carbon capture and sequestration, disaster management and generally, manage resources in a territory and provide more accurate information on environmental and anthropic phenomena. Such applications demand the study, the conception and the development of new machine learning approaches especially tailored for Earth Observation Data in order to face numerous challenges such as spatial and temporal domain adaptation, transfer learning, time series analysis, multi-source/multi-view information. Such applications may include unsupervised, weakly-supervised and supervised scenarios with, probably, noisy and scarce data. Unfortunately, there is still a lack of interaction between domain experts and machine learning researchers. The two communities, currently, are not yet able to structure themselves around these issues due to a lack of mutual knowledge.
The Special Issue will supply the opportunity to raise the awareness of the Machine Learning community about issues and challenges related to Earth Observation Data and, at the same time, it will attract people from the Earth Observation community to get in touch with the Machine Learning community and the Machine Learning journal.
This special issue is a follow up of the MACLEAN19 workshop (http://ceur-ws.org/Vol-2466/) but it is open to new original research submissions in the area of Machine Learning for Earth Observation Data.
Topics of the special issue include but are not limited to:
* 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
* December 1st, 2019: Submission System Opening
* May 15, 2020: Submission System Closing (extended)
* August 15th, 2020: First Round of Review
* November 15th, 2020: Resubmission of the manuscripts
* January 15th, 2021: Second Round of Review
* March 15th, 2021: Resubmission of the manuscripts
* April 15th, 2021: Final Decision and Publication
PAPER SUBMISSION AND REVIEW:
- Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
- All papers will be reviewed following standard reviewing procedures for the Journal.Authors should follow the “Instructions for Authors”
- Papers must be prepared in accordance with the Journal guidelines: www.springer.com/10994 .
- Submit manuscripts to: http://MACH.edmgr.com; Select “Machine Learning for Earth Observation Data” as the article type when submitting.