# The Big Picture: An Improved Method for Mapping Shipping Activities
## 7 Conclusions and Future Work  
In this work, a prototype of a AIS data handling pipeline capable of producing improved density maps was presented. First, a series of filters were presented in order to clean the raw AIS messages and restrict the data to the appropriate area and timeframe of interest. Furthermore, two techniques for improving the AIS data, completing it with additional vessel positions, were described. The first is a graph-based technique for reconstructing trajectories when large temporal gaps occur in the data, while the seconds extracts vessel detections from satellite imagery. Finally, the pipeline allows for creating a spatial grid according to the user's requirements and calculating the cumulative time the vessels spent within its cells, as well as the generation of the density map visualization of the results. In order to reconstruct trajectories with large temporal gaps between their messages, a gap-filling mechanism was also introduced. Based on historical data, this process repurposes the A* algorithm so that patterns of movement of past trajectories are considered when discovering the path the vessel followed during the AIS gap. Experiments on real AIS data have shown that our method results in a significant improvement over the linear interpolation approach, up to 15% and 26% for temporal gaps of 3-6 and 6-24 h, respectively. This allows for the improvement of AIS datasets and consequently the creation of more accurate density visualizations. Furthermore, the multi-step process of extracting geo-referenced vessel detections from optical and SAR images relies on the combinations of detailed preprocessing, a CNN architecture and a fusion mechanism. The proposed approach allows for not only completing AIS trajectories with additional vessel positions, but also discover \"dark vessels\", whose positions cannot be accounted for by the AIS. Real world data from Copernicus and AIS stations were used to demonstrate the effectiveness of this mechanism. In the future, we intend to study the effect of limiting the historical data used during the trajectory reconstruction to the vessel type in question, for a result more suited to the occasion. Additionally, life-long learning mechanisms will be utilized so that recent  
Figure 18: Satellite coverage based on Sentinel-1 orbit for the month of October 2021, indicating limited coverage in the high seas. The yellow rectangle is the area of interest for this study. activity has a larger role in the gap-filling process, by appropriately updating the penalty weights. Also, a framework will be designed to operate such reconstructions on a larger scale, combining the different techniques for even more accurate results. Finally, other sources for monitoring vessel movement not captured by AIS or satellite imagery, like cameras for smaller boats near the coast, can be easily incorporated into our framework, resulting in more accurate maps. Conceptualization, D.Z. ; Methodology, A.T.-K., D.Z., K.B., M.V., G.S. and G.K.; Software, A.T.-K., D.Z., K.B., M.V., G.S. and G.K.; Writing-original draft, A.T.-K., D.Z., K.B., M.V., G.S. and G.K.; Writing-review & editing, A.T.-K., D.Z., K.B., M.V., G.S. and G.K. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This work was supported by the European Maritime and Fisheries Fund (EMFF) through service contract No. CINEA/EMFF/2020/3.1.16/Lot2/SI2.850940, and by European Union's Horizon 2020 research and innovation program under grant agreement No. 101092749, project \"Critical Action Planning over Extreme-Scale Data (CREXDATA)\". Third Party Data-Restrictions apply to the availability of these data. Data were obtained from MarineTraffic (marinetraffic.com, (accessed on 6 September 2023)). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: