Collaborative Research: ABI Innovation: Dark Ecology: Deep Learning and Massive Gaussian Processes to Uncover Biological Signals in Weather Radar
合作研究:ABI 创新:黑暗生态:深度学习和大规模高斯过程揭示天气雷达中的生物信号
基本信息
- 批准号:1661329
- 负责人:
- 金额:$ 30.93万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-05-15 至 2020-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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 20 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 25-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.
每年春天和秋天,数十亿只鸟在美国各地迁徙,大部分是在夜幕的掩护下。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移。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20年里发生了变化。一个结果将是我们更好地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份全面的25年大气图像档案,提供了有关鸟类运动的基线信息。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进行一项结合计算机视觉和机器学习方法的新研究,以从整个美国气象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,以估计在任何给定夜晚迁徙的鸟类数量,测量鸟类种群的模式和趋势,并进行假设驱动的科学研究。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,并将吸引科学家、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。Dark Ecology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与降水和雷达数据中的其他杂波。领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(GP)模型,以创建平滑的迁移密度和速度的国家地图。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雷达数据集,并分析超过2亿份存档雷达扫描。由此产生的数据和工具将按照开放获取政策进行整理,并由研究小组用于开展有关大陆尺度迁移模式和驱动因素的生态研究。项目信息可在http://darkecology.cs.umass.edu上找到。
项目成果
期刊论文数量(17)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Aeroecology of a solar eclipse
日食的航空生态学
- DOI:10.1098/rsbl.2018.0485
- 发表时间:2018
- 期刊:
- 影响因子:3.3
- 作者:Nilsson, Cecilia;Horton, Kyle G.;Dokter, Adriaan M.;Van Doren, Benjamin M.;Farnsworth, Andrew
- 通讯作者:Farnsworth, Andrew
Decline of the North American avifauna
- DOI:10.1126/science.aaw1313
- 发表时间:2019-10-04
- 期刊:
- 影响因子:56.9
- 作者:Rosenberg, Kenneth V.;Dokter, Adriaan M.;Marra, Peter P.
- 通讯作者:Marra, Peter P.
Time of emergence of novel climates for North American migratory bird populations
- DOI:10.1111/ecog.04408
- 发表时间:2019-06
- 期刊:
- 影响因子:5.9
- 作者:F. L. La Sorte;D. Fink;A. Johnston
- 通讯作者:F. L. La Sorte;D. Fink;A. Johnston
Projected changes in wind assistance under climate change for nocturnally migrating bird populations
气候变化下夜间候鸟种群风力援助的预计变化
- DOI:10.1111/gcb.14531
- 发表时间:2018
- 期刊:
- 影响因子:11.6
- 作者:La Sorte, Frank A.;Horton, Kyle G.;Nilsson, Cecilia;Dokter, Adriaan M.
- 通讯作者:Dokter, Adriaan M.
Broad-Scale Weather Patterns Encountered during Flight Influence Landbird Stopover Distributions
- DOI:10.3390/rs12030565
- 发表时间:2020-02-01
- 期刊:
- 影响因子:5
- 作者:Clipp, Hannah L.;Cohen, Emily B.;Buler, Jeffrey J.
- 通讯作者:Buler, Jeffrey J.
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Steven Kelling其他文献
Steven Kelling的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Steven Kelling', 18)}}的其他基金
ABI Sustaining: eBird: Maintaining the Cyberinfrastructure to Support the Collection, Storage, Archive, Analysis, and Access to a Global Biodiversity Data Resource
ABI 维持:eBird:维护网络基础设施以支持全球生物多样性数据资源的收集、存储、存档、分析和访问
- 批准号:
1356308 - 财政年份:2014
- 资助金额:
$ 30.93万 - 项目类别:
Continuing Grant
Collaborative Research: ABI Development: Advancing Map of Life's Impact and Capacity for Sharing, Integrating, and Using Global Spatial Biodiversity Knowledge
合作研究:ABI 开发:推进生命影响地图和共享、整合和使用全球空间生物多样性知识的能力
- 批准号:
1262396 - 财政年份:2014
- 资助金额:
$ 30.93万 - 项目类别:
Continuing Grant
SoCS: Collaborative Research: A Human Computational Approach for Improving Data Quality in Citizen Science Projects
SoCS:协作研究:提高公民科学项目数据质量的人类计算方法
- 批准号:
1209589 - 财政年份:2012
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: CDI-Type II: BirdCast: Novel Machine Learning Methods for Understanding Continent-Scale Bird Migration
合作研究:CDI-Type II:BirdCast:用于理解大陆规模鸟类迁徙的新型机器学习方法
- 批准号:
1125098 - 财政年份:2011
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
RAPID: Gulf Coast Oil Spill Biodiversity Tracker. A Volunteer-based Observation Network to Monitor the Impact of Oil on Organisms along the Gulf Coast
RAPID:墨西哥湾沿岸漏油生物多样性追踪器。
- 批准号:
1049363 - 财政年份:2010
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
"The Biodiversity Analysis Pipeline"
“生物多样性分析管道”
- 批准号:
0734857 - 财政年份:2008
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Multi-Scaled Data in Ecology: Scale Dependent Patterns in the Environment
生态学中的多尺度数据:环境中的尺度依赖模式
- 批准号:
0542868 - 财政年份:2006
- 资助金额:
$ 30.93万 - 项目类别:
Continuing Grant
SEI+II:Ecological Discovery & Inference: Tools for Data-driven Exploration and Testing of Observational Data
SEI II:生态发现
- 批准号:
0612031 - 财政年份:2006
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
ITR-(ASE+EVS)- (dmc+sim): Tracking Environmental Change through the Data Resources of the Bird-monitoring Community
ITR-(ASE EVS)- (dmc sim):通过鸟类监测社区的数据资源跟踪环境变化
- 批准号:
0427914 - 财政年份:2004
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
The Science Knowledge and Education Network Building a User Base around Scientific Publications: Editing Online Content and Annotating Scientific Materials
科学知识和教育网络围绕科学出版物建立用户群:编辑在线内容和注释科学材料
- 批准号:
0435016 - 财政年份:2004
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
相似国自然基金
Research on Quantum Field Theory without a Lagrangian Description
- 批准号:24ZR1403900
- 批准年份:2024
- 资助金额:0.0 万元
- 项目类别:省市级项目
Cell Research
- 批准号:31224802
- 批准年份:2012
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research
- 批准号:31024804
- 批准年份:2010
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research (细胞研究)
- 批准号:30824808
- 批准年份:2008
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
相似海外基金
Collaborative Research: Sustainable ABI: Arctos Sustainability
合作研究:可持续 ABI:Arctos 可持续性
- 批准号:
2034568 - 财政年份:2021
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: FuTRES, an Ontology-Based Functional Trait Resource for Paleo- and Neo-biologists
合作研究:ABI 创新:FuTRES,为古生物学家和新生物学家提供的基于本体的功能性状资源
- 批准号:
2201182 - 财政年份:2021
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Development: Symbiota2: Enabling greater collaboration and flexibility for mobilizing biodiversity data
协作研究:ABI 开发:Symbiota2:为调动生物多样性数据提供更大的协作和灵活性
- 批准号:
2209978 - 财政年份:2021
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets
合作研究:ABI 创新:大规模神经形态数据集的计算探索
- 批准号:
2028361 - 财政年份:2020
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Enabling machine-actionable semantics for comparative analyses of trait evolution
合作研究:ABI 创新:启用机器可操作的语义以进行特征进化的比较分析
- 批准号:
2048296 - 财政年份:2020
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Development: Integrated platforms for protein structure and function predictions
合作研究:ABI开发:蛋白质结构和功能预测的集成平台
- 批准号:
2021734 - 财政年份:2020
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Biofilm Resource and Information Database (BRaID): A Tool to Fuse Diverse Biofilm Data Types
合作研究:ABI 创新:生物膜资源和信息数据库 (BRaID):融合多种生物膜数据类型的工具
- 批准号:
2027203 - 财政年份:2019
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Development: Building a Pipeline for Validation, Curation and Archiving of Integrative/Hybrid Models
合作研究:ABI 开发:构建集成/混合模型的验证、管理和归档管道
- 批准号:
1756250 - 财政年份:2018
- 资助金额:
$ 30.93万 - 项目类别:
Continuing Grant
Collaborative Research: ABI Development: The next stage in protein-protein docking
合作研究:ABI 开发:蛋白质-蛋白质对接的下一阶段
- 批准号:
1759472 - 财政年份:2018
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Quantifying biogeographic history: a novel model-based approach to integrating data from genes, fossils, specimens, and environments
合作研究:ABI 创新:量化生物地理历史:一种基于模型的新颖方法来整合来自基因、化石、标本和环境的数据
- 批准号:
1759729 - 财政年份:2018
- 资助金额:
$ 30.93万 - 项目类别:
Standard Grant