UC5 — Graph-Based Classification of AIS Time-Series Data

Objectives

Understand how AIS vessel tracking data is structured
Learn why graph representations outperform flat time series for vessel classification
Compare three GNN architectures: GCN, GraphSAGE, GAT
See how the framework supports both CPU and GPU training

Repository: wp7-UC5-ais-classification-gnn Contributors: Xue-Cheng Tai, Gro Fonnes (NORCE Research)

The Problem

Maritime surveillance generates large volumes of Automatic Identification System (AIS) data — position, speed, heading, and identity for thousands of vessels. A key task is classifying vessel activities, particularly distinguishing fishing from non-fishing behavior, which matters for:

Fisheries management and quota enforcement
Environmental monitoring
Maritime safety

Traditional approaches use hand-crafted features and thresholds, but vessel movement patterns are complex and context-dependent.

Key Insight: Graphs Instead of Sequences

Instead of treating AIS data as a flat time series, UC5 transforms each vessel’s trajectory into a graph structure:

        graph LR
    subgraph "Traditional Approach"
        A[t1] --> B[t2] --> C[t3] --> D[t4] --> E[t5]
    end
    subgraph "UC5 Graph Approach"
        F((t1)) --- G((t2))
        F --- H((t3))
        G --- H
        G --- I((t4))
        H --- I
        I --- J((t5))
    end

Representation	Captures
Flat time series	Temporal order only
Graph structure	Spatial + temporal relationships between trajectory points

Nodes represent time steps. Edges encode spatial and temporal relationships between points. This richer representation captures patterns that flat sequences miss.

GNN Architectures

Three graph neural network architectures were evaluated:

Model	Architecture	Description
GCN	Graph Convolutional Network	Spectral-based graph convolutions
GraphSAGE (GSG)	Sample and Aggregate	Inductive representation learning
GAT	Graph Attention Network	Attention-weighted neighbor aggregation

All models are built on the Deep Graph Library (DGL) and support both CPU and GPU training with CUDA 11.8.

Results

Model	Test Accuracy
GCN	94.4%
GraphSAGE	94.4%
GAT	93.1%

GraphSAGE achieved the best performance at 94.4% test accuracy on fishing vs. non-fishing classification.

The key finding: representing AIS data as graphs instead of sequences mattered more than the choice of GNN architecture. The same idea applies to other domains with spatial-temporal data.

Framework Features

Feature	Details
Data pipeline	AIS CSV → graph construction → DGL dataset
Training	Configurable via YAML (model type, learning rate, epochs)
Evaluation	Accuracy, precision, recall, F1-score
GPU support	CUDA 11.8 with automatic fallback to CPU
Validation	Pydantic-based configuration validation

Quick Start

git clone https://github.com/NAICNO/wp7-UC5-ais-classification-gnn.git
cd graph-based-classification-of-ais-time-series-data
pip install -r requirements.txt
jupyter notebook demonstrator-v1.orchestrator.ipynb

Keypoints

AIS data tracks vessel position, speed, heading, and identity
Transforming trajectories into graphs captures spatial-temporal patterns flat time series miss
Three GNN architectures evaluated: GCN, GraphSAGE, GAT
GraphSAGE achieves 94.4% accuracy on fishing vs. non-fishing classification
Data representation mattered more than model architecture choice
Built on DGL with CPU and GPU (CUDA 11.8) support