EAGER-NEON: Image-Based Ecological Information System (IBEIS) for Animal Sighting Data for NEON

EAGER-NEON：用于 NEON 动物观察数据的基于图像的生态信息系统 (IBEIS)

• 批准号: 1550880
• 负责人: Charles Stewart
• 金额: $ 10.6万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550880&HistoricalAwards=false
• 关键词: EAGER; NEON; Image; Based; Ecological

The National Ecological Observatory Network (NEON) is coming online and will provide atmospheric and ecological data locally, regionally and continent wide. At the same time, images are rapidly becoming the most abundant, widely available, and cheapest source of information about the natural world, especially about animals. This project will extend NEON's data, scientific, and citizen science capacity with image-based animal sighting data to scalably collect, manage, and analyze data for individually identifiable wildlife using the Image-Based Ecological Information System (IBEIS) prototype recently developed under another NSF award. Combined with other ecological data, the image data offer the promise of addressing big questions about animal ecology, behavior, and conservation - who? where? when? what? and why? - at high resolution and at fine-grained scale, across landscapes and ecosystems, from an individual animal to regional and global systems. As part of this project, undergraduate and graduate students from ecology and computer science at four institutions will produce and test the application interface, and will develop a suite of companion applications and training tools to allow greater involvement of citizen scientists.These tools will allow NEON to connect its database to data derived from large volumes of animal photographic images. Although this is primarily a proof of concept proposal focused on connecting whale shark images to NEONs atmospheric data, it will provide the means to be able to apply IBEIS algorithms and databases on images of distinctly marked North American species such as tortoises, monarch butterflies, salamanders, spotted skunk, bobcat, lynx, and humpback whales, thereby connecting these to NEON?s other data streams related to organisms, land use, hydrology and biogeochemistry. The proposed suite of tools includes: 1. an infrastructure and a mechanism for collecting images from scientists, automated remote cameras, citizen scientists and other sources; 2. a data management system for storing, accessing and manipulating images and derived data; 3. computer vision techniques for extracting information from the images about the identity of species and individual animals, as well as techniques for combining that information with other relevant data to derive information about ecological units such as animals, populations, species, and habitats; 4. a software application-program interface integrating the image and derived data with and within NEON; 5. a framework for engaging citizen scientists in data collection, derived science, and interaction with nature. Previous funding from NSF allowed building and testing of an IBEIS prototype. This project will focus on the detection and identification methods for the identifiable US species, on integrating the system with NEON, and on scaling the system to many thousands of daily images from a variety of sources.

国家生态观测网（氖）即将上线，将提供地方、区域和整个大陆的大气和生态数据。与此同时，图像正迅速成为关于自然世界，特别是关于动物的最丰富、最广泛、最便宜的信息来源。该项目将扩展氖的数据，科学和公民科学能力，基于图像的动物目击数据，可扩展地收集，管理和分析数据，使用基于图像的生态信息系统（IBEIS）的原型最近开发的另一个NSF奖。结合其他生态数据，图像数据提供了解决有关动物生态，行为和保护的大问题的承诺-谁？在哪儿？什么时候？你说什么？为什么呢- 以高分辨率和精细的尺度，跨越景观和生态系统，从单个动物到区域和全球系统。作为该项目的一部分，来自四个机构的生态学和计算机科学的本科生和研究生将制作和测试应用程序界面，并将开发一套配套应用程序和培训工具，以使公民科学家能够更多地参与，这些工具将使氖能够将其数据库与从大量动物摄影图像中获得的数据连接起来。虽然这主要是一个概念证明的建议，重点是连接鲸鲨图像的NEON大气数据，它将提供的手段，能够应用IBEIS算法和数据库的图像明显标记的北美物种，如乌龟，帝王蝶，蝾螈，斑点臭鼬，山猫，山猫，座头鲸，从而连接到氖？其他数据流涉及生物体、土地利用、水文学和地球化学。建议的工具套件包括：1。用于从科学家、自动远程相机、公民科学家和其他来源收集图像的基础设施和机制; 2.数据管理系统，用于存储、访问和操纵图像和衍生数据; 3.计算机视觉技术，用于从图像中提取有关物种和个体动物身份的信息，以及将该信息与其他相关数据相结合以获得有关生态单元（如动物，种群，物种和栖息地）的信息的技术; 4.软件应用程序接口，其将所述图像和导出的数据与氖集成并且在所述NEON内集成; 5.让公民科学家参与数据收集、衍生科学和与自然互动的框架。此前，NSF的资助允许建立和测试IBEIS原型。 该项目将侧重于可识别的美国物种的检测和识别方法，将该系统与氖集成，并将该系统扩展到来自各种来源的数千张日常图像。