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

国家生态观测站网络（NEON）即将上线，将提供本地、区域和整个大陆的大气和生态数据。与此同时，图像正迅速成为有关自然世界（尤其是有关动物）的最丰富、最广泛且最便宜的信息来源。该项目将通过基于图像的动物观测数据扩展 NEON 的数据、科学和公民科学能力，以使用最近在另一个 NSF 奖项下开发的基于图像的生态信息系统 (IBEIS) 原型来大规模收集、管理和分析可单独识别的野生动物的数据。与其他生态数据相结合，图像数据有望解决有关动物生态、行为和保护的重大问题 - 谁？在哪里？什么时候？什么？为什么？ - 高分辨率和细粒度，跨越景观和生态系统，从个体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专业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，以允许公民科学家更多地参与。这些工具将使 NEON 能够将其数据库与来自大量动物摄影图像的数据连接起来。虽然这主要是一个概念验证提案，重点是将鲸鲨图像与 NEON 大气数据连接起来，但它将提供一种方法，能够将 IBEIS 算法和数据库应用于明显标记的北美物种（如乌龟、帝王蝶、蝾螈、斑点臭鼬、山猫、山猫和座头鲸）的图像，从而将这些图像与 NEON 与生物、土地利用、水文学和生物地球化学相关的其他数据流连接起来。拟议的工具套件包括： 1. 用于从科学家、自动远程摄像机、公民科学家和其他来源收集图像的基础设施和机制； 2. 用于存储、访问和操作图像及派生数据的数据管理系统； 3. 从图像中提取有关物种和个体动物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动物、种群、物种和栖息地等生态单位信息的技术； 4. 将图像和派生数据与 NEON 集成并在 NEON 内集成的软件应用程序接口； 5. 让公民科学家参与数据收集、衍生科学以及与自然互动的框架。 NSF 之前提供的资金允许构建和测试 IBEIS 原型。 该项目将重点关注可识别的美国物种的检测和识别方法，将系统与 NEON 集成，并将系统扩展到来自各种来源的数千张日常图像。

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.