Identification of Anomalous Geospatial Trajectories via Persistent Homology

TL;DR

This paper introduces a topological data analysis method using persistent homology to detect anomalies like crop circles in geospatial trajectories by identifying loops in embedded spatiotemporal data.

Contribution

It is the first to apply persistent homology to spatiotemporal geospatial data for anomaly detection, specifically identifying loops as anomalies.

Findings

Effective detection of loops in trajectories indicating anomalies

Robustness to perturbations and shape variations

Applicable to maritime, environmental, and surveillance data

Abstract

We present a novel method for analyzing geospatial trajectory data using topological data analysis (TDA) to identify a specific class of anomalies, commonly referred to as crop circles, in AIS data. This approach is the first of its kind to be applied to spatiotemporal data. By embedding $2+1$-dimensional spatiotemporal data into $\mathbb{R}^3$, we utilize persistent homology to detect loops within the trajectories in $\mathbb{R}^2$. Our research reveals that, under normal conditions, trajectory data embedded in $\mathbb{R}^3$ over time do not form loops. Consequently, we can effectively identify anomalies characterized by the presence of loops within the trajectories. This method is robust and capable of detecting loops that are invariant to small perturbations, variations in geometric shape, and local coordinate projections. Additionally, our approach provides a novel perspective on anomaly detection, offering enhanced sensitivity and specificity in identifying atypical patterns in geospatial data. This approach has significant implications for various applications, including maritime navigation, environmental monitoring, and surveillance.