Machine Learning Provides SeaVision with Maritime Behavior Recognition

Abstract

Commercial satellite data collection enables vessel tracking via Automatic Identification System (AIS) observations with global coverage and high revisit rates at relatively low cost. USG maritime analysts can use such data for monitoring and analysis via maritime domain awareness (MDA) tools. While the coverage and revisit rates make these commercial data relevant for addressing a wide range of maritime domain problems, the resulting large data volume and velocity hamper human analysis from fully exploiting these data. This presents an opportunity for application of machine learning (ML) analytics to automate low-level detection of activity of interest from the data streams. This would help analysts more quickly answer time-sensitive questions and focus on decision-making instead of manual data analysis. For DARPA’s Geospatial Cloud Analytics (GCA) program, we developed flexible ML analytics for recognizing maritime behaviors from AIS data. These analytics extend our Multi-INT Analytics for Pattern Learning and Exploitation (MAPLE) family of ML capabilities. We deployed these analytics as a cloud-based service (MAPLE as a Service – MaaS) to perform automated detection of potential illegal, unreported, and unregulated (IUU) fishing activity at tactical time scales. We validated our algorithms via a quantitative performance evaluation. We demonstrated that our approach achieves comparable performance to published results for state-of-the-art fishing detection algorithms. The baseline state-of the-art approaches are fishing gear type specific (i.e., one approach for longliner, another for trawler, and a third for purse seiner gear). In contrast, MaaS uses a single approach for all of these fishing gear types. This evaluation process used an anonymized AIS data set with fishing behavior ground truth provided by Global Fishing Watch for model training and validation. We also employed commercial AIS data from two different regions (U.S. Pacific Islands & coast of Gabon) for additional performance evaluation. Since MaaS learns behavior models from relevant examples, we can apply MaaS to other maritime behavior recognition problems (e.g., mine laying, piracy, and research vessel activity, as well as multi-vessel behavior like transshipment or aggressive vessel formations) without having to design new algorithms for each problem. The cloud-based analytics as a service approach enables rapid integration with multiple data sources and consumers. This approach makes use of open standards that enable machine-to-machine interaction for data access, analytics processing, and analytic product publication. It admits ready extension of existing MDA tools to enhance human-machine interactions. We are currently working to integrate a prototype MaaS capability for automatically detecting fishing behaviors from AIS data with the USG web-based SeaVision MDA tool.