Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks
Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks
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DOI：
10.1002/rse2.92
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发表时间：
2019-03-01
影响因子：
5.5
通讯作者：
Kelly, Jeffrey
中科院分区：
文献类型：
--
作者：
Chilson, Carmen;Avery, Katherine;Kelly, Jeffrey
虽然NEXRAD雷达已被证明是检测机载动物的有效工具，但在雷达图像中检测生物现象通常涉及手动，耗时的数据提取过程。本文的重点是应用机器学习来自动找到雷达数据，这些数据可以在鸟类（特别是紫马丁和树燕）集体离开栖息地时拍摄大量鸟类的快照。这些聚集在雷达图像中很明显，在清晨鸟类离开栖息地时出现反射率升高的环。我们的目标是开发一种算法，可以确定单个雷达图像是否至少包含一个紫马丁或树燕的栖息地。我们使用已知栖息地的数据集来训练三种机器学习算法，它们采用(1)传统的人工神经网络（ANN），(2)称为Inception-v3的复杂预先存在的卷积神经网络（CNN），以及(3)从头构建的浅CNN。由此产生的程序在寻找鸟类栖息地方面都是有效的，在AUC高于0.9的情况下，浅层CNN和Inception-v3网络在90%的时间里都能做出正确的判断。据我们所知，这项研究是第一次将神经网络应用于分析雷达图像中的鸟类栖息地，这些分析工具为研究飞行动物的生态和行为提供了新的途径，并在风力发电场安置、空中交通管理和野生动物保护方面有实际应用。NEXRAD雷达网络提供了大量的大陆尺度数据档案，并有可能捕获整个脊椎动物种群。我们将现有的机器学习模型应用于一个新的数据集，这构成了一个有价值的方法来从这个档案中提取信息。
Although NEXRAD radars have proven to be an effective tool for detecting airborne animals, detecting biological phenomena in radar images often involves a manual, time- consuming data-extraction process. This paper focuses on applying machine learning to automatically find radar data that snapshots large aggregations of birds (specifically Purple Martins and Tree Swallows) as they depart en masse from roosting sites. These aggregations are evident in radar images as rings of elevated reflectivity that appear early in the morning as birds depart from roost sites. Our goal was to develop an algorithm that could determine whether an individual radar image contained at least one Purple Martin or Tree Swallow roost. We use a dataset of known roost locations to train three machine learning algorithms that employed (1) a traditional Artificial Neural Network (ANN), (2) a sophisticated preexisting Convolutional Neural Network (CNN) called Inception-v3, and (3) a shallow CNN built from scratch. The resulting programs were all effective at finding bird roosts, with both the shallow CNN and the Inception-v3 network making correct determinations about 90 per cent of the time with an AUC above .9. To the best of our knowledge, this study is the first to apply neural networks in the analysis of bird roosts in radar imagery, and these analytical tools offer new avenues of research into the ecology and behavior of flying animals, with practical applications to wind farm placement, air traffic administration and wildlife conservation. The NEXRAD radar network offers a tremendous archive of continental-scale data and has the potential to capture entire vertebrate populations. We apply existing machine learning models to a new dataset which constitutes a valuable approach to extracting information from this archive.