构建复杂林地环境中难检异常的航拍图像数据集，助力搜救与追捕。
An aerial color image anomaly dataset for search missions in complex forested terrain
- 基于真实搜救场景采集高分辨率航拍图像，标注难以察觉的异常目标。
- 现有检测方法在该数据集上表现不佳，准确率低于40%。
- 适合研究林地环境下的异常检测、众包标注与交互式分析的学者。
德国农村发生一起家庭谋杀案后，警方在广阔森林中未能找到嫌疑人。为协助搜寻，一架科研飞机拍摄了高分辨率航拍影像。由于植被茂密遮挡微小线索，自动化分析失效，转而启动众包搜寻。该过程生成了一个独特且标注完整的异常图像数据集，涵盖被遮挡的真实复杂环境。该数据集可作为复杂林地环境下异常检测算法的基准，支持追捕与救援任务。初步基准测试显示现有方法表现较差，凸显对上下文感知方法的需求。数据集已公开，支持离线处理。此外，还提供交互式网络界面，支持在线浏览与动态扩展，用户可标注并提交新发现。
原文摘要 · Abstract (English)
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.
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