The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (APIoU = .50) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches.

中文翻译：

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使用天气雷达数据中的时空信息来检测和跟踪公共栖息地

飞行动物离开栖息地的情况通常可以在天气雷达数据中看到不断扩大的环形图案。例如，NEXRAD 网络存档了 143 个连续的美国雷达站超过 25 年的数据，为研究鸟类和蝙蝠的栖息地点和时间以及生态系统提供了机会。然而，对这些信息的访问受到手动注释数百万次雷达扫描的成本的限制。我们开发并部署了人工智能辅助系统来注释雷达数据中的栖息地。我们构建带有栖息地注释的数据集，以支持自动检测模型的训练和评估。栖息地被检测、跟踪并纳入我们开发的基于网络的界面中，用于人类筛选，以产生研究级注释。我们部署该系统是为了从五大湖周围的 12 个雷达站收集燕子和马丁鸟栖息地的信息，时间跨度长达 21 年。在验证了系统的实用价值后，我们建议通过结合体积雷达扫描的空间和时间通道来改进探测器。五大湖雷达扫描的部署允许在 612 786 次雷达扫描中加速注释 15 628 个栖息地特征，需要 183.6 个小时的人工筛查时间，或每次雷达扫描 1.08 秒。我们估计部署的系统将人工注释时间减少了约 7 倍。时间检测器模型提高了交集阈值 0.5 的平均精度（AP借条 = .50）比之前的模型（48%→56%）提高了 8%，在试点部署中进一步将人类筛选时间减少了 2.3 倍。这些数据包含有关燕子和马丁鸟以及正在经历急剧下降的空中食虫动物物种的物候和种群趋势的重要信息，并使得新的研究成为可能。我们提出了误差分析，为对这些物种进行大陆规模的历史调查奠定了基础，并为自动检测雷达数据中其他科特定现象（例如蝙蝠栖息地和蜉蝣孵化）提供了起点。