The rapid proliferation of tracking sensors—ranging from vessel and vehicle tracking systems to smartwatches, cameras, and Earth observation sensors—has led to an unprecedented influx of high-frequency, high-volume data. Yet, despite this abundance, many trajectories remain incomplete, contain errors, or are entirely missing. A vast reservoir of tracking data remains unexplored or underutilized, holding valuable insights that could enhance monitoring and decision-making. The MUlti-Sensor Inferred Trajectories (MUSIT) project is dedicated to unlocking this potential by integrating and refining data from heterogeneous sources. Through advanced AI algorithms and spatio-temporal methodologies, MUSIT reconstructs and enhances trajectories, filling in gaps and minimizing errors to provide a more accurate and insightful picture of moving objects’ behavior. By fusing multi-sensor data, MUSIT not only improves trajectory accuracy but also enriches it with semantic information, adding context and meaning to movement patterns. The project explores cross-domain representation models within the ICT sector, pushing the boundaries of what is possible in trajectory analysis. --- The MMDEC dataset contains data from various sources and sensors (AIS, satellite images, meteorology, oceanography, ports locations, marine protected areas,...) within an Area of Interest (AOI) covering the western Celtic Sea, the English Channel, and a part of the North Sea, and a 3-month time from July 1, 2023, to October 1, 2023.