United Nations/Costa Rica Workshop on Machine Learning applied to Space Weather and Global Navigation Satellite Systems

16 - 20 February 2026
San José, Costa Rica

 
Jointly organized by
the United Nations Office for Outer Space Affairs (UNOOSA) and
the Space Research Center (CINESPA), University of Costa Rica

Co-organized and co-sponsored by
the International Committee on Global Navigation Satellite Systems (ICG) and
the Committee on Space Research (COSPAR)

 


Available Information

 

Introduction

Machine learning (ML) is a key application of Artificial Intelligence (AI) that is focused on enabling algorithms to automatically learn from data and identify patterns. This is achieved through various learning methods, including: Artificial Neural Networks (ANNs), Evolutionary Algorithms, Decision Tree Classifiers, Clustering Algorithms, or Fuzzy Logic Inference, among others. Effective data treatment and management are also crucial for ML. These techniques allow ML algorithms to extract valuable insights and correlations from complex data sets.

Space Weather (SWx) events might influence the performance and reliability of space-borne and ground-based technological systems and endanger life and health in space. It might affect communication and navigation systems, damage electrical grids, harm satellite electronics, and expose passengers flying over the poles and at high altitudes to increased radiation levels. Moreover, the technology of global navigation satellite systems (GNSS) is currently being used in a wide range of sectors, including but not limited to: mapping and surveying, monitoring of the environment, agriculture and natural resources management, disaster warning and emergency response, aviation, maritime, and land transportation. For GNSS users, understanding the impact of space weather on the ionosphere is crucial, as it can significantly affect signal accuracy and reliability. At the GNSS space segment, four global constellations are operational. On the ground, thousands of permanent GNSS stations and millions of Internet-of-things (IoT) devices, including smartphones, have contributed to deploying a "de facto" large IoT GNSS receiver network. Hence, the application of ML on the data produced by this global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications.

To address the application of ML to SWx and GNSS and to focus on initiating pilot projects and strengthening the networking of these topics related to institutions in the Latin American region, a Workshop on machine learning applied to space weather and GNSS will be held in San José from 16 to 20 February 2026. This workshop is being organized by the United Nations Office for Outer Space Affairs in cooperation with the Space Research Center (CINESPA) of the University of Costa Rica. The University of Costa Rica will host the workshop on behalf of the Government of the Republic of Costa Rica. The workshop is co-organized and co-sponsored by the International Committee on GNSS (ICG) and the Committee on Space Research (COSPAR), and supported by the Faculty of Exact Sciences and Technology (FACET) of the National University of Tucumán (UNT) and the International Centre for the Theoretical Physics (ICTP).

 

Objectives and expected outcomes

The main objectives of the workshop will be to reinforce the exchange of information between countries and scale up the capacities in the region, pursuing the application of ML to SWx and GNSS technology solutions; share information on national, regional, and global projects and initiatives, which could benefit regions; and enhance cross-fertilization among those projects and initiatives.

The specific objectives of the workshop will be to introduce the use of ML tools to analyse SWx and GNSS data; promote the greater exchange of actual experiences with specific applications; focus on appropriate ML, SWx and GNSS applications projects at the national and/or regional levels; and define recommendations and findings to be forwarded as a contribution to the Office for Outer Space Affairs and ICG, particularly, in forging partnerships to strengthen and deliver capacity-building on satellite navigation science and technology.

The expected outcomes of the workshop will be recommendations and findings on discussed topics to be adopted by the workshop participants; preliminary agreement of cooperation between countries in the region and action plan addressing identified issues/concerns.

The discussions at the workshop will also be linked to the 2030 Agenda for Sustainable Development and to its targets set out for Sustainable Development Goals (SDG), such as:

  • SDG 3: Good health and wellbeing - GNSS positioning enables individual patients, staff or equipment to be monitored, and response teams directed more efficiently;
  • SDG 7: Affordable and clean energy - GNSS reflectometry techniques can produce scatterometry models to assist in the optimum positioning of offshore wind farms;
  • SDG 9: Industry, Innovation and Infrastructure - SWx forecasting and monitoring can be improved using ML algorithms, which can help to enable better decision-making for space-based infrastructure and operations; GNSS signals can be used for navigation and positioning of in-orbit space operations, particularly from low Earth orbit to cis-lunar;
  • SDG 11: Sustainable Cities and Communities - GNSS is widely used for urban planning to pinpoint structures and reference points for cadastral and urban planning purposes; and
  • SDG 17: Partnerships for the Goals - Use of ML methods applied to SWx and GNSS to facilitate international cooperation and partnerships, such as collaborative research and development of SWx forecasting and monitoring systems.

 

Preliminary Programme of the Workshop

The workshop programme will include plenary sessions and sufficient time for discussions among participants to identify the priority areas where pilot projects should be launched and examine possible partnerships that could be established. The Local Organizing Committee will arrange a half-day technical/cultural tour during the workshop. As a preliminary suggestion, the following sessions will be organized:

Thematic Sessions
Session 1: Introduction and Overview to Space Weather, GNSS and Machine Learning
  • This session will provide a series of introductory talks to the subjects of the workshop: Machine Learning, Space Weather and GNSS, setting the stage for the technical sessions that follow, and providing a platform for experts to share their experience and insights.
Session 2: GNSS-based applications focusing on, but not limited to
  • Advances and performance benefits due to multi-sensor integration of GNSS applications;
  • The use of GNSS for aviation, including integration of satellite navigation technology into air traffic management and airport surface navigation and guidance;
  • The use of navigation and timing systems for road, rail, and engineering applications, including vehicle guidance, geographic information system mapping, and precision farming;
  • Navigation systems operation in the marine environment, including waterway navigation, harbour entrance/approach, marine archaeology, fishing, and recreation;
  • Commercial applications of GNSS.
Session 3 (Hands-on Seminar): Basic Machine Learning techniques
  • Provide a guided series of hands-on exercises where participants can apply basic Machine Learning techniques to Space Weather/GNSS sample datasets.
Session 4: GNSS data processing
  • Exploring the use of Machine Learning algorithms for de-noising and enhancing GNSS signals;
  • Using Machine Learning to mitigate Space Weather and scintillation effects on GNSS signals;
  • Using Machine Learning algorithms for spectrum protection, interference detection and mitigation.
Session 5: Use of Machine Learning for Space Weather forecasting
  • Use of Machine Learning algorithms for predicting Space Weather events, including solar flares, coronal mass ejections, geomagnetic storms, and their effects in the Earth upper atmosphere.
  • Use of ensemble methods for combining predictions of multiple Machine Learning models to improve the accuracy of Space Weather forecasting.
  • Effects of Space Weather on GNSS performance and accuracy, and monitoring and forecasting of these effects by scintillation receivers, models, and other means.
Session 6: Capacity building, training and education in the field of Machine Learning, Space Weather and GNSS
  • Education opportunities at different levels/needs;
  • GNSS education tools/open source software related to GNSS, Space Weather and Machine Learning.
Discussion Sessions
  • Challenges and opportunities in applying Machine Learning to Space Weather and GNSS: issues, concerns and approaches, including future direction and open research questions.
  • Possible follow-up projects, initiatives and proposals for future workshops/training courses/technical seminars.

 

Additional Resources

17th International Symposium on Equatorial Aeronomy, 9 - 13 February 2026, Liberia, Costa Rica
Website (External Link): https://www.iap-kborn.de/isea17/home

 

 

Copyright ©2025 UNOOSA, All Rights Reserved