Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data play a significant role in tracking vessel activity and mapping mobility patterns such as those found during fishing activities. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology, it is shown how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry, highlighting changes in the vessel’s moving pattern, which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. We propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall F-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the geometry observed in the trajectory.

In the maritime environment, the Automatic Identification System (AIS) contains information related to vessel trajectories that can be used to detect unusual maritime occurrences and maritime traffic patterns. To detect such occurrences with supervised learning methods the AIS messages must be manually annotated, which can be a demanding process. Therefore, unsupervised methods are used to identify anomalous traffic patterns based on vessel trajectories. Typically, dense regions of maritime activity are studied to capture common traffic patterns which help identify trajectories that do not follow the norm. However, these approaches cannot detect anomalous behaviors along common pathways or incorporate time-related events into the analysis. Such challenges motivate the approach taken in this work by using auto-regressive techniques to model vessel trajectories and clustering analyses to explore behavior patterns of vessels. Results confirm that the Auto-regressive Integrated Moving Average (ARIMA) and Ornstein-Uhlenbeck (OU) processes are able to model the trajectories and can be used with density-based spatial clustering of applications with noise (DBSCAN), hierarchical clustering (HC), and spectral clustering (SC) to identify different behavioral patterns.

As governments take measures against the COVID-19, the epidemic situation is expected to improve. Consequently, public sentiment is likely to fluctuate during this change, influencing the best course of action. The detection of public sentiment can help the service of various industries and the government’s subsequent formulation of measures and policies. In recent times, social media has become a prevalent way for the public to express emotions and opinions. Therefore, sentiment analysis of social media texts may detect and provide a valuable indication of public sentiment. We present Senti-COVID19, an interactive visual analytic system for reflecting and analyzing public sentiment and detecting sentiment fluctuation triggers on social media. Compared to other visual analytic tools, Senti-COVID19 focuses on sentiment analysis of COVID-19 related tweets and classifies them based on the dominant sentiment category. The system emphasizes analyzing tweets that mainly discuss the epidemic situation with keywords and hashtags related to COVID-19. Senti-COVID19 adopts lexicon-based sentiment analysis to divulge the public sentiment to COVID-19 events, employing libraries to extract keywords and statistics for providing detailed information. In addition, it offers visualization views for presenting the analysis results and extracted information.We design evaluations and a usage scenario, which we used to assess the effectiveness of Senti-COVID19. The results show that Senti-COVID19 can effectively analyze sentiment from social media text and provide interactive visual analysis for users to explore derived data and identify insights from the front-end.

In the maritime environment, the Automatic Identification System (AIS) is used to monitor vessel activity concerning security and safety ocean-wide. AIS data has been used to detect anomalous behaviors related to suspicious activities and hazardous events. Typically, clustering analysis is used to investigate anomalous events within the AIS data stream. However, the main challenge in this approach is to determine and execute the dissimilarity measure between trajectories since they differ in size and time. In addition, these calculations are computationally expensive and not scalable. To tackle this issue, compression algorithms can be applied to perform clustering analysis since they are typically used to reduce storage and processing time. Therefore, the proposed analysis will assess how compression algorithms affect clustering results with respect to detecting anomalous vessel trajectories. The analysis results show that a suitable compression algorithm can reduce the overall processing time with little impact on the clustering results while supporting the scalability of this type of analysis.

The movement of vessels through both national and international waters are of interest to both security and defense personnel. In international waters, vessel monitoring takes place for the benefit...