Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019
Call for Papers
The Earth and Space environments are being monitored by an unprecedented amount of sensors: Earth observation satellites, sensor networks, telescopes working in different wavelengths, human records of Earth and Space events, etc. This generates a huge amount of raw data that must be processed in order to yield insights and knowledge about these environments.
Data can be as varied as the sensors and applications for it, ranging from well-structured time-series of spatial data such as data cubes, to spectra in different resolutions from objects in the sky, to semi-structured data collected from different types of sensor networks. Even social networks can be used to monitor reports on Earth and Space phenomena.
Machine learning methods can be used to process Earth and Space data in different ways: to extract knowledge from data, to transform data in different representations, to search for patterns on data, to compare data to models or other observational data, etc. This workshop will address the different practical aspects of application of machine learning technologies to data related to the Earth and Space environments, covering algorithms, methodologies, applications and case studies.
Topics of Interest
The workshop welcomes contributions on applications of machine learning for Environmental and Space Data, including (but not limited to):
- Machine learning applications to Remote Sensing and Earth Observation data (image segmentation and classification, time series clustering and classification, spatio-temporal data analysis, etc.);
- Machine learning applications to Space Observation data (image processing and classification, spectra classification, time series analysis and classification, etc.);
- Volunteered Geographic Information (VGI) and citizen engagement in machine learning models for space and Earth observation data.
- Novel algorithms for machine learning and applications, and different approaches and uses of classical algorithms;
- HPC for machine learning for Environmental and Space Data;
- Environmental and Space Observation Data representation, storage and retrieval for computing-intensive machine learning;
- Data intensive computing applied to Earth and Space Observation data in general;
- Case studies and Experiences.
Important Deadlines
- Paper submission: March 17th, 2019
- Notification of Acceptance: March 31st, 2019
- End of Early-bird Registration: May 8th, 2019
- Camera Ready Submission: May 8th, 2019
- ICCSA 2019 Conference: July 1st-4th, 2019
Submissions
To submit a paper, please connect to the Submission site from the link available at the ICCSA 2019 web site. Only papers submitted through the electronic system and strictly adhering to the relevant format will be considered for reviewing and publication. The paper must deal with original and unpublished work, not submitted for publication elsewhere. All submissions will be reviewed by at least three experts in the relevant field. The submitted paper must be camera-ready, between 10 and 16 pages long and formatted according to the LNCS rules. Please consult the formatting information and templates. Please pay attention, when submitting your contribution, to select the entry Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019 in the listbox shown in the submission form.
Proceedings
Accepted and presented papers will be published as part of the conference proceedings by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) Series.
Organising Committee
- Rafael Santos, National Institute for Space Research, Brazil
- Karine Reis Ferreira, National Institute for Space Research, Brazil
Programme Committee (Confirmed)
- Thales Sehn Körting (National Institute for Space Research, Brazil)
- Victor Maus (Vienna University of Economics and Business, Austria)
- Tessio Novack (Heidelberg University, GIScience Research Group, Germany)
- João Pires (UNL, Portugal)
- Ádamo Santana (Fuji Electric, Japan)
- Alexander Zipf (Heidelberg Institute for Geoinformation Technology, Germany)