Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.

中文翻译：

---

使用无人驾驶飞机系统和计算机视觉近乎实时地监测涉水鸟


由于航空测量方法的发展以及使用计算机视觉模型来识别和分类单个生物体，在大面积地理区域进行野生动物种群监测变得越来越可行。但是，航空测量仍然不经常进行，并且在获取航空影像和将其转换为种群监测数据之间通常存在较长的延迟。近乎实时的监测对于主动管理决策和生态预测越来越重要。要实现大比例的实现，需要将机载影像、用于将影像处理为单个生物体信息的计算机视觉模型，以及确保影像在采集后快速处理为数据所需的自动化工作流。在这里，我们展示了对美国佛罗里达州大沼泽地的涉水鸟进行近乎实时监测的端到端工作流程。使用无人驾驶飞机系统（又名无人机）每周一次地获取图像，处理成正射马赛克（使用 Agisoft metashape），使用 Retinanet-50 对象检测器转换为个体级别的物种数据，进行后处理、存档，并在基于 Web 的可视化平台上呈现（使用 Shiny）。工作流程的主要组成部分是使用 Snakemake 自动化的。底层计算机视觉模型为六种目标物种中的五种（白鹮、大白鹭、大蓝鹭、木鹳和粉红琵鹭）提供准确的对象检测、物种分类以及总数和物种水平计数。该模型对雪鹭的表现不佳，因为标签数量少，难以将它们与白鹮（数量最多的物种）区分开来。 通过自动化调查后处理，这些物种的种群数据可以近乎实时地获得（从调查之日起 <1 周），从而在生态预测和主动管理所需的时间尺度上提供信息。