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Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. Such tracking sensors include but are not limited to vessel, airplane or vehicle tracking data, drones, smartwatches and smart bands as well as cameras and earth observation sensors. Despite the overabundance of data generated by the tracking devices, there are still cases where the trajectory of a moving object (e.g., vessel or human) has gaps, errors, or is unavailable. Consequently, there is a vast pool of tracking data that remains unexplored or underutilized, but has the potential of revealing important information. The "MUlti-Sensor Inferred Trajectories" (MUSIT) project aims to advance trajectory mining from a multimodal perspective addressing challenges that might arise from single- or multi-source tracking data This project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101182585. This page reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains. Partners involved: EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON (ICCS) - Coordinator, Greece Consiglio Nazionale Delle Ricerche (CNR), Italy University of the Aegean (UAegean), Greece Technische Universiteit Eindhoven (TU/e), the Netherlands École Navale (ENV), France Nodalpoint Systems (NPS), Greece Akkodis, France San José State University Research Foundation (SJSU), USA École de Technologie Supérieure (ETS), Canada University of Southern California (USC), USA