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

国家生态观测网（NEON）即将上线，将提供当地、区域和整个大陆的大气和生态数据。与此同时，图像正迅速成为关于自然世界，特别是关于动物的最丰富、最容易获得和最便宜的信息来源。该项目将扩展NEON的数据、科学和公民科学能力，利用基于图像的动物观测数据，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）原型，为单独可识别的野生动物收集数据。结合其他生态数据，图像数据提供了解决有关动物生态学，行为和保护的重大问题的希望-谁？在哪里?什么时候?怎么啦？,为什么?-在高分辨率和细粒度尺度上，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，来自四所院校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，以允许更多的公民科学家参与。这些工具将允许NEON将其数据库与来自大量动物摄影图像的数据连接起来。虽然这主要是一个将鲸鲨图像与NEON大气数据连接起来的概念验证提案，但它将提供能够将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）的图像的手段，从而将这些与NEON连接起来？与生物、土地利用、水文学和生物地球化学有关的其他数据流。建议的工具套件包括：1。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. 一种数据管理系统，用于存储、存取和操纵图像和派生数据；3. 从图像中提取物种和动物个体身份信息的计算机视觉技术，以及将这些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. 一个软件应用程序接口集成图像和衍生数据与NEON；5. 一个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。国家科学基金会先前的资助允许建造和测试IBEIS原型。该项目将专注于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.