{"ID":2892195,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15492","arxiv_id":"2507.15492","title":"An aerial color image anomaly dataset for search missions in complex forested terrain","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.","short_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 initiati...","url_abs":"https://arxiv.org/abs/2507.15492","url_pdf":"https://arxiv.org/pdf/2507.15492v1","authors":"[\"Rakesh John Amala Arokia Nathan\",\"Matthias Gessner\",\"Nurullah Özkan\",\"Marius Bock\",\"Mohamed Youssef\",\"Maximilian Mews\",\"Björn Piltz\",\"Ralf Berger\",\"Oliver Bimber\"]","published":"2025-07-21T10:52:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}