Monitoring technology trends is essential for the European Space Agency (ESA) to fulfill its mission of advancing Europe’s space capabilities. Yet, in a rapidly evolving ecosystem where information is scattered across various databases and formats this is no easy task.… Read More »Lost in space but not in data: Tracking Technology Trends in the Space Field
Existing vision-text contrastive learning models enhance representation transferability and support zero-shot prediction by matching paired image and caption embeddings while pushing unrelated pairs apart. However, astronomical image-label datasets are significantly smaller compared to general image and label datasets available from… Read More »CosmoCLIP: Generalising Large Vision-Language Models for Astronomical Imaging
Due to the potential of Large Language Models (LLMs) to disrupt the way people interact with information systems across numerous industries, we have investigated options to extend functionality in the context of astronomy science archives. A frequent request by the… Read More »Enhancing object-type searches in ESA Astronomy Science Archives extending ESASky AI capabilities with LLM and Retrieval Augmented Generation
This study explores the decision making capabilities of Large Language Model (LLM) AI agents to automate learning in planetary landing missions. In particular, the work investigates the use of AI agents to minimise human intervention in training a lunar lander… Read More »Decision Making for Planetary Landing Applications using AI Agents and Reinforcement Learning
Integrating Earth observation, meteorological, and sensor data into domain-specific research often requires substantial pre-existing knowledge. Leveraging Large Language Models (LLMs) to interpret natural language prompts offers a solution, enabling scientists in fields such as climate science, biology, humanities, and economics… Read More »LLM-powered Assistants for Advanced Geospatial Dataset Recommendations based on Geolocated Queries