VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery

TL;DR

This paper evaluates feature-based visual anomaly detection methods on planetary surface imagery, introducing new benchmarks for lunar and Mars data, and demonstrates their effectiveness for resource-constrained space exploration applications.

Contribution

It provides the first empirical evaluation of VAD methods on planetary imagery and introduces two new benchmarks for lunar and Mars surface anomaly detection.

Findings

VAD methods effectively identify rare planetary phenomena.

Edge-oriented VAD solutions are suitable for onboard deployment.

Benchmarks facilitate future research in planetary anomaly detection.

Abstract

Space missions generate massive volumes of high-resolution orbital and surface imagery that far exceed the capacity for manual inspection. Detecting rare phenomena is scientifically critical, yet traditional supervised learning struggles due to scarce labeled examples and closed-world assumptions that prevent discovery of genuinely novel observations. In this work, we investigate Visual Anomaly Detection (VAD) as a framework for automated discovery in planetary exploration. We present the first empirical evaluation of state-of-the-art feature-based VAD methods on real planetary imagery, encompassing both orbital lunar data and Mars rover surface imagery. To support this evaluation, we introduce two benchmarks: (i) a lunar dataset derived from Lunar Reconnaissance Orbiter Camera Narrow Angle imagery, comprising of fresh and degraded craters as anomalies alongside normal terrain; and (ii) a Mars surface dataset designed to reflect the characteristics of rover-acquired imagery. We evaluate multiple VAD approaches with a focus on computationally efficient, edge-oriented solutions suitable for onboard deployment, applicable to both orbital platforms surveying the lunar surface and surface rovers operating on Mars. Our results demonstrate that feature-based VAD methods can effectively identify rare planetary surface phenomena while remaining feasible for resource-constrained environments. By grounding anomaly detection in planetary science, this work establishes practical benchmarks and highlights the potential of open-world perception systems to support a range of mission-critical applications, including tactical planning, landing site selection, hazard detection, bandwidth-aware data prioritization, and the discovery of unanticipated geological processes.