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

每年春天和秋天，数十亿只鸟类在美国各地迁徙，主要是在黑暗的掩护下。美国气象雷达网络收集的数据和新的分析方法使我们能够跟踪这些迁徙。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年中发生了变化。结果之一将是我们更有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。美国气象雷达网络记录了一个全面的20年大气图像档案，提供了有关鸟类活动的基线信息。从图像中提取生物信息目前还不是自动化的，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结合计算机视觉和机器学习的方法进行新的研究，以从整个美国天气雷达数据档案中解锁有关鸟类迁徙的详细信息。由此产生的数据集将免费提供，为研究人员提供信息资源，以估计任何给定夜晚的鸟类迁徙数量，测量鸟类种群的模式和趋势，并进行假设驱动的科学。该研究将推进大规模科学问题的大数据分析和可视化技术，并将让科学家、保护规划者、学生和公众参与有关鸟类迁徙的数据、可视化和教育材料。黑暗生态学将利用大规模云计算，开发新的计算机视觉、机器学习、以及雷达分析方法来测量美国各地候鸟的密度和速度。深度卷积网络将被训练来区分候鸟和雷达数据中的降水和其他杂波。域转移和弱监督训练的新技术将使卷积网络的训练只需要中等规模的训练集。将开发高斯过程模型，以绘制平滑的国家移徙密度和速度图。新颖的GP方法和云计算工作流程将使我们能够扩展到大量的雷达数据集，并分析超过2亿个存档的雷达扫描。由此产生的数据和工具将通过开放获取政策进行管理，并由研究团队用于对大陆规模移民的模式和驱动因素进行生态研究。项目信息可以在http://darkecology.cs.umass.edu上找到。

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.