Collaborative Research: EAGER: Prototype of an Image-Based Ecological Information System (IBEIS)

合作研究：EAGER：基于图像的生态信息系统（IBEIS）原型

• 批准号: 1453428
• 负责人: Daniel Rubenstein
• 金额: $ 5.96万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453428&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Prototype; Image

Images are rapidly becoming the most abundant, widely available, and cheapest source of information about the natural world. Images taken by field scientists, tourists, and incidental photographers, and gathered from camera traps and autonomous vehicles provide rich data with the promise of addressing big ecological questions at high resolution and at fine-grained scale. Realizing this potential requires building a large autonomous computational system that starts from image collections and progresses all the way to answering ecological queries, such as population sizes, species distributions and interactions, and movement patterns. The system must have methods of extracting the relevant ecological information from the images and of integrating with other ecological data sources, with minimal human interaction, using state-of-the art information management, computer vision, and data analytics technologies. Such a system will advance computer systems and simultaneously enable ecology to develop as a science of connections across spatial, temporal, and biological scales, as well as provide data- and scientifically-grounded support for ecological decisions. This work aims to build a prototype of an Image-Based Ecological Information Software System (IBEIS) that relies on a proliferation of images collected daily on a single facility from many different sources, both human and automatic, to determine both the species as well as recognition of distinct individuals. The system will allow for tracking location and movement while providing a data management system that will allow scientists to better understand, and at finer granularity, behaviors and motivations. The system will include: (1) an infrastructure and a mechanism for collecting images from tourists and other sources; (2) a (cloud) infrastructure and a data management system for storing, accessing, and manipulating the images and the derived data; (3) computer vision techniques for extracting information from the images about the identity of individual units, as well as techniques for combining that information with other relevant data to derive information about meaningful ecological units; and (4) statistical techniques and query structures to support ecological queries of the data, such as population sizes and dynamics, movement history and home ranges, and species interactions. This work will advance computer systems including information management, computer vision, and data analytics technologies, all the while increasing public engagement in science and ecology.

图像正迅速成为关于自然世界的最丰富、最广泛、最便宜的信息来源。由实地科学家、游客和偶然的摄影师拍摄的图像，以及从相机陷阱和自动驾驶汽车收集的图像，提供了丰富的数据，有望以高分辨率和细粒度解决重大生态问题。实现这一潜力需要建立一个大型的自主计算系统，从图像收集开始，一直发展到回答生态查询，如人口规模，物种分布和相互作用以及运动模式。该系统必须具备从图像中提取相关生态信息并与其他生态数据源集成的方法，最大限度地减少人为交互，使用最先进的信息管理，计算机视觉和数据分析技术。这样一个系统将推进计算机系统，同时使生态学能够发展成为一门跨空间、时间和生物尺度的科学，并为生态决策提供数据和科学基础的支持。这项工作的目的是建立一个基于图像的生态信息软件系统（IBEIS）的原型，该系统依赖于每天从许多不同的来源（包括人类和自动）收集的图像，以确定物种以及识别不同的个体。该系统将允许跟踪位置和移动，同时提供一个数据管理系统，使科学家能够更好地了解，并在更细的粒度，行为和动机。该系统将包括：（1）用于从游客和其他来源收集图像的基础设施和机制;（2）用于存储、访问和操纵图像和衍生数据的（云）基础设施和数据管理系统;（3）用于从图像中提取关于各个单元的身份的信息的计算机视觉技术，以及将该信息与其他相关数据相结合以获得有关有意义的生态单位的信息的技术;以及（4）支持数据的生态查询的统计技术和查询结构，例如种群大小和动态、运动历史和家域以及物种相互作用。这项工作将推进计算机系统，包括信息管理，计算机视觉和数据分析技术，同时增加公众对科学和生态的参与。