An aerial color image anomaly dataset for search missions in complex forested terrain

TL;DR

This paper introduces a new annotated aerial image dataset of anomalies in dense forests, aimed at improving search and rescue operations through better detection methods in complex, occluded environments.

Contribution

It provides a unique, publicly accessible dataset with real-world, occluded anomalies and an interactive platform for ongoing annotation and benchmarking.

Findings

Existing detection methods perform poorly on the dataset

The dataset highlights the need for context-aware anomaly detection

Open access facilitates further research and development

Abstract

After a family murder in rural Germany, authorities failed to locate the suspect in a vast forest despite a massive search. To aid the search, a research aircraft captured high-resolution aerial imagery. Due to dense vegetation obscuring small clues, automated analysis was ineffective, prompting a crowd-search initiative. This effort produced a unique dataset of labeled, hard-to-detect anomalies under occluded, real-world conditions. It can serve as a benchmark for improving anomaly detection approaches in complex forest environments, supporting manhunts and rescue operations. Initial benchmark tests showed existing methods performed poorly, highlighting the need for context-aware approaches. The dataset is openly accessible for offline processing. An additional interactive web interface supports online viewing and dynamic growth by allowing users to annotate and submit new findings.