# The Big Picture: An Improved Method for Mapping Shipping Activities
###### Abstract  
Density maps support a bird's eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3-6 and 6-24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting \"dark vessels\", i.e., vessel positions not present in the AIS data. m 202315508  
Sonar and satellite imagery, and are able to extract the location and movement of vessels through processing their input [4]. Recently, the advancements of remotely operated and autonomous vehicles have lead to some works on the constant monitoring areas near the coast [5]. Such systems do not require the aid or even the permission of the vessels to operate and thus are more suitable for studying illegal or malicious activity [6]; however, their accuracy largely depends on hardware specifications, the coverage over an area of interest and post-processing methods. On the other hand, the second group is comprised from systems whose function depends on the cooperation of the vessels themselves. In other words, these systems work through the transmission of positional messages from the vessels, allowing others to record their movement. A few systems have been introduced and are operating today, with the most prominent ones being the Long-range Identification and Tracking (LRIT) and the Automatic Identification System (AIS) [7; 8]. The main reason that vessels use such systems to transmit their positions, or other relevant information, is mainly to ensure safety while travelling. Regardless of the effectiveness of any system, capturing the accurate and complete trajectory of a moving object is almost impossible in real conditions. This is mainly due to the inherent limitations of data acquisition and storage mechanisms used. As a result, the continuous movement of an object is usually obtained as an approximate form of discrete samples of spatio-temporal locations [9]. Sometimes the error is acceptable for a given use case while in other occasions it may lead to erroneous interpretations. Since being declared as mandatory for large vessels in 2004, the most commonly used datasets for vessel tracking have been based on the AIS. Besides their location, the vessels broadcast their identification information, characteristics and destination, along with other information originating from on-board devices and sensors, such as their speed and heading [9; 10; 11]. Due to the large number of vessels transmitting their positions globally, advanced analysis is required to interpret these data. Towards an improved understanding of such datasets, visualizations through density maps is commonly used. The added value of density maps is that they support a bird's eye view of maritime traffic, through providing an overview of vessel behavior either at a regional or a global scale. From density maps, one can determine the patterns of life in a given study area.