GNN for asynchronous Spatio-Temporal Data Classification

This paper presents and explores a novel approach for solving classification tasks on asynchronous spatio-temporal data by encoding both spatial and temporal dependencies into a single graph structure. Time-series data are represented as chains of nodes connected by directed edges, capturing the sequential nature of the data. Additional edges are introduced to model interactions between time series, enabling the method to account for dependencies and relationships across multiple entities. This approach is applied to an example from the Maritime Situational Awareness domain, where the goal is to classify vessel types using tracks from the Automatic Identification System (AIS). Over the AIS maritime vessels transmit real-time information, including their position, speed, course, and general vessel details, for collision prevention and identification purposes. The method considers not only the movement patterns of individual vessels but also their interactions with other vessels and their tracks, which are critical for capturing complex maritime behaviors. This shall lay the basis for training Graph Neural Networks (GNN) with Graph Convolutions to perform classification tasks. This is the first step to model interactions and dependencies in spatio-temporal data, providing a promising framework for advancing classification tasks in dynamic, multi-entity systems.