# The Big Picture: An Improved Method for Mapping Shipping Activities
###### Abstract
Patterns of life are understood as observable human activities that can be described as patterns in the maritime domain related to a specific activity (e.g., fishing) [9]. Essentially, vessel-based maritime activity can be described in space and time while being classified to a number of known activities at sea (fishing etc.). The spatial element describes recognized areas where maritime activity takes place, including ports, fishing grounds, offshore energy infrastructure, dredging areas, etc. The transit paths to and from these areas also describe the spatial element (e.g., commercial shipping, ferry routes), while the temporal element often holds additional information for categorizing these activities (e.g., fishing period, time of year, etc.) [12]. In this setting, we identify two main tasks that we address in this paper. The first task is to design and develop a pipeline capable of producing sound and clear interpretations of shipping activity in a given area of interest from noisy and often inaccurate AIS data, extending the high-level description in [13]. We design and implement a number of algorithmic methods that support the handling of these data and their transformation into accurate density maps. We provide modules for the data transformation process, from data cleaning and smoothing, to the final result visualization. In light of an intensified use of maritime space, there will be a growing need for such applications in the near future. The second task is to create mechanisms for improving the quality of the AIS datasets, by including additional vessel positions. Such mechanisms would result in more complete datasets and in turn would assist in generating more accurate density map depictions of vessel activity. Two techniques are described for this task. First, a novel method for trajectory gap filling is proposed. This method leverages historical data to restore missing positional messages in any given ship trajectory, improving the completeness and quality of the underlying data. In order to evaluate our approach, we validated its performance on a real-world dataset of AIS messages, covering European waters from October of 2021. The results indicate that our suggested method can effectively reconstruct a ship trajectory with higher accuracy (up to 26%), compared to a straight-line interpolation. Furthermore, we present a dedicated mechanism that is able to extract vessel positions based on satellite imagery. This mechanism is suitable for both radar and optical data, allowing for the monitoring of vessels in cases where the AIS does not. This approach is based on a Convolutional Neural Network (CNN) architecture and allows for extracting vessel coordinates in cases where AIS data are not available, making an excellent complementing data source. Preliminary results of this latter framework can also be observed in [14]. The overall framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets and producing highly accurate density maps. Specific components of the framework, demonstrate several advantages over existing algorithms, while the overall complete framework is unique for producing this type of visualization for AIS data. The contributions of this work can be described as follows:  
* We provide an overview of the AIS system and how it can be used to capture vessel movement, while also highlighting some of the issues with the fitness of AIS data. * We describe two mechanisms for improving the quality of an input AIS dataset, in cases of significant temporal gaps in the trajectories. The first mechanism suggests the most probable path the vessel followed during its AIS-messages gap, using historical mobility information over the area of interest. The other leverages satellite imagery to enrich our original dataset with additional vessel positions through an accurate detection technique based on CNN architecture.