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

arXiv 2507.15492 ↗First posted 21 Jul 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 99 references that are in the map. First posted on arXiv 21 Jul 2025.

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25 of 99 references.

References · 25

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13 more
  1. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  2. 2021An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioningcited by 32
  3. 2025The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detectioncited by 30
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  5. 2020Search and Rescue with Airborne Optical Sectioningcited by 20
  6. 2022Anomaly Detection in Aerial Videos with Transformerscited by 11
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  13. 2022Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videoscited by 1

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