In the current accelerated technological ecosystem driven by advancements in Artificial Intelligence (AI) and Machine Learning (ML), there is an articulated demand for secure data operations (DataOps) and learning (AIOps or MLOps).
On 23 June 2025, the MobiSpaces Impact Event brought together stakeholders in Rome and online to mark the culmination of three years of research and innovation under the Horizon Europe programme.
The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed.
This book, co-authored by Mahmoud Sakr , Alejandro Vaisman , Esteban Zimányi covers the key topics in mobility data analysis, with all steps of the pipeline illustrated by real-world examples.
This demo paper introduces MobiML, a new library that aims to help scientists and engineers with developing mobility ML solutions using trajectory data.
Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection.
This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks.
Federated Learning for Mobility Applications. MobiSpaces mentioned in acknowledgements. Relevant work for mobility AI & data spaces
The rapidly evolving field of mobility data spaces, integral to the contemporary technological landscape, creates unique challenges and opportunities in the context of legal compliance.
