Overview

The Earth and Space Sciences are characterised by an exponential growth in data volume and complexity, and are derived from diverse sources such as satellite imagery, in-situ measurements, and simulations. Alongside AI, Europe invests heavily on Earth Observation and Space Programmes. Through Copernicus, a flagship initiative of the European Union, as well as via considerable and sustained contributions towards EUMETSAT, ESA, ECMWF and elsewhere, Europe has become a hub for high quality data and services. These programs, supported by substantial financial and technological resources, provide a crucial foundation for advancing scientific understanding, addressing global challenges and fueling innovation. This abundance of data presents both unprecedented opportunities for scientific discovery and significant challenges in data management, analysis, and interpretation.

This workshop aims to bring together researchers exploring how AI methods, including machine learning, symbolic approaches, and their intersection, can be effectively applied to transform, explain, fuse, or otherwise prepare large and complex datasets from diverse sources within Earth & Space Sciences for use and reuse in downstream applications. In addition, the workshop welcomes contributions to AI-powered semantic data annotation and metadata enrichment, due to their significance in improving data discoverability, understandability, and reusability.

List of relevant topic areas

A non-exhaustive tentative list of topics relevant to this session is the following: 

• Data Preprocessing and Enhancement: AI-driven techniques for preprocessing and enhancing diverse datasets, including noise reduction, feature extraction, and data augmentation.
• Data Fusion and Integration: Address the challenges and solutions related to integrating data from multiple sources, including satellite, aerial, and ground-based sensors, using AI-based fusion techniques. 
• Time-Series Analysis: AI models and approaches for analysing time-series data from various sources in Earth & Space Sciences.
• Data Reusability and Open Data Initiatives: AI for complementing best practices for making Earth & Space Science data more accessible, reusable, and interoperable.
• Semantic Data Annotation: AI-driven semantic annotation, metadata enrichment, and knowledge extraction to improve data discovery and understanding in Earth & Space Sciences.
• Data Quality Assessment: AI-driven data quality assessment, anomaly detection, and error correction in Earth & Space Science datasets.
• AI-Enhanced Data Products: AI-powered data products and services that leverage Earth & Space Science data for various sectors.
• AI for Data Search and Retrieval: Discuss AI algorithms and technologies that enhance the search and retrieval of relevant Earth & Space Science data from large archives.
• Data Privacy and Ethics: Ethical and privacy considerations when applying AI to generate Earth & Space Science data derivatives, including issues related to personal privacy, consent, and data security.

Submission Guidelines

We welcome:

• full papers (8-12 pages), and
• extended abstracts and short articles (2-6 pages)

The proceedings of the Workshop will be published as CEUR Workshop Proceedings (CEUR-WS.org).

All contributions are expected to be formatted in the CEURART style, according to CEUR-WS requirements. 

Selected contributions may be published as a Special Issue in an International Journal, depending on participation and quality. Confirmation and more details will be provided at a later date.

Committees

Program Committee

• Mohanad Albughdadi, European Center for Medium-Range Weather Forecasts (ECMWF), Germany
• Vasileios Baousis, European Center for Medium-Range Weather Forecasts (ECMWF), UK
• Omar Barrilero, European Union Satellite Centre (SATCEN), Spain
• Lorenzo Bruzzone, University of Trento, Italy
• Mihai Datcu, German Aerospace Center (DLR), Germany
• Fani Deligianni, University of Glasgow, UK
• Begüm Demir, Technical University of Berlin, Germany
• Zoe Falomir, Umeå University, Sweden
• Sandro Fiore, University of Trento, Italy
• Stelios Karozis, NCSR “Demokritos”, Greece
• Spyros Mouzakitis, National Technical University of Athens, Greece
• Christian Pagé, CERFACS, France
• Ioannis Papoutsis, National Technical University of Athens, Greece
• Claudia Paris, University of Twente, The Netherlands
• Dimitrios Skoutas, Athena Research Center, Greece
• Rafael Tolosana-Calasanz, University of Zaragoza, Spain

Organizing committee

• Iraklis A. Klampanos, University of Glasgow, UK
• Antonis Troumpoukis, National Centre for Scientific Research "Demokritos", Greece
• Manolis Koubarakis, National and Kapodistrian University of Athens, Greece