Collaborative Research: ABI Innovation: Dark Ecology: Deep Learning and Massive Gaussian Processes to Uncover Biological Signals in Weather Radar

每年春秋两季，数十亿只鸟在美国各地迁徙，大多是在黑暗的掩护下迁徙。美国天气雷达网络和新的分析方法收集的数据让我们能够追踪这些迁徙。黑暗生态项目将开发新的资源，使我们能够估计候鸟的密度，因为它们在过去25年里发生了变化。一个结果将是我们有更好的能力监测鸟类种群及其迁徙系统，以及各种环境因素的影响。美国天气雷达网络记录了20年来大气图像的全面档案，其中提供了鸟类活动的基线信息。目前，从图像中提取生物信息并不是自动化的，这使得它非常缓慢和低效。一个由生态学家和计算机科学家组成的团队将进行一项新的研究，将计算机视觉和机器学习的方法结合起来，从整个美国天气雷达数据档案中解开有关鸟类迁徙的详细信息。由此产生的数据集将是免费的，为研究人员提供信息资源，以估计在任何给定夜晚迁徙的鸟类的数量，测量鸟类种群的模式和趋势，并进行假设驱动的科学。这项研究将推进大规模科学问题的大数据分析和可视化技术，并将吸引科学家、保护规划者、学生和普通公众提供关于鸟类迁徙的数据、可视化和教育材料。黑暗生态将利用大规模云计算，开发新的计算机视觉、机器学习和雷达分析方法，以测量全美候鸟的密度和速度。将训练深度卷积网络，以区分候鸟与降雨量和雷达数据中的其他杂波。域转移和弱监督训练的新技术将使仅用中等大小的训练集来训练卷积网络成为可能。将开发高斯过程(GP)模型，以创建平滑的国家迁徙密度和速度地图。新的GP方法和云计算工作流程将使我们能够扩展到海量雷达数据集，并分析超过2亿次存档的雷达扫描。由此产生的数据和工具将根据开放获取政策进行管理，并由研究小组用来进行关于大陆范围移民模式和驱动因素的生态研究。有关项目信息，请访问http://darkecology.cs.umass.edu.。

Every spring and fall billions of birds migrate across the US, largely under the cover of darkness. Data collected by the US network of weather radars and new analysis methods let us track these migrations. The Dark Ecology Project will develop new resources allowing us to estimate the densities of migrating birds as they have changed in the last 25 years. One outcome will be our better ability to monitor bird populations and their migration systems, and the impacts of various environmental factors. The US network of weather radars has recorded a comprehensive 20-year archive of images of the atmosphere, which provides the baseline information about bird movements. Extracting biological information from the images is not automated currently, making it very slow and inefficient. A team of ecologists and computer scientists will conduct novel research combining methods in computer vision and machine learning to unlock detailed information about bird migration from the entire US archive of weather radar data. The resulting dataset will be freely available, providing an information resource for researchers to estimate the number of birds migrating on any given night, measure the patterns and trends of bird populations, and do hypothesis driven science. The research will advance big data analysis and visualization techniques for large-scale science questions, and will engage scientists, conservation planners, students, and the general public with data, visualizations, and educational material about bird migration.Dark Ecology will leverage large-scale cloud computing and develop novel computer vision, machine learning, and radar analysis methods to measure the densities and velocities of migrating birds across the US. Deep convolutional networks will be trained to discriminate migrating birds from precipitation and other clutter in the radar data. New techniques for domain transfer and weakly supervised training will enable the training of convolutional networks with only modest-sized training sets. Gaussian process (GP) models will be developed to create smooth national maps of migration density and velocity. Novel GP methods and cloud-computing workflows will allow us to scale to massive radar data sets and analyze the more then 200 million archived radar scans. The resulting data and tools will be curated with open access policies, and used by the research team to conduct ecological research about patterns and drivers of continent-scale migration. Project information can be found at http://darkecology.cs.umass.edu.