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

Princeton University, Princeton NJ

Investigators

Abstract

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.