September 2, 2014: UIC CS Associate Professor Tanya Berger-Wolf received a new NSF grant in computational ecology

UIC CS Associate Professor Tanya Berger-Wolf, heading the collaboration with Daniel Rubenstein (Ecology and Evolutionary Biology, Princeton) and Charles Stewart (CS, RPI), has received a new NSF grant in computational ecology entitled: Collaborative Research: EAGER: Prototype of an Image-Based Ecological Information System (IBEIS).

UIC's portion is $128,292 of the $314,504 award total. This award starts September 1 , 2014 and ends August 31, 2016.

Abstract:

IBIES: Image-Based Ecological Information System
http://compbio.cs.uic.edu/~tanya/ibeis/
"Creating a digital view of the natural world through the lenses of individual cameras."
"From images to ecological insight and conservation action."

Images are rapidly becoming the most abundant, widely available, and cheapest source of information about the natural world. These images are taken by field scientists, tourists, and incidental photographers, and are gathered from camera traps and autonomous vehicles. Such data offer the promise of addressing big questions about animal ecology, behavior, and conservation 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 and conservation queries, such as population sizes, species distributions and interactions, and movement patterns. The system must have methods of extracting the information about the plants, animals and habitat 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. When this is achieved we can truly develop ecology as a science of connections across spatial, temporal, and biological scales, as well as provide data- and scientifically-grounded support for conservation decisions.

We propose to build the first prototype of an Image-Based Ecological Information System (IBEIS). We will start with images fro a single facility of large animals with distinctive striped, spotted, wrinkled or notched markings, such as elephants, giraffes and zebras. We will build on our existing individual animal identification software (HotSpotter) and the state-of-the-art automatic image analysis methods to determine who the animals are (species and individual), where they are, and when they are there, to produce a record of ecologically relevant information. We will leverage and expand the existing ecological data management system (WildBook) to provide mechanisms for storing and querying this data, as well as combining it with relevant geographic, environmental, behavioral and climate data to enable the determination of what the animals are doing, and why they are doing it. We will provide a cloud infrastructure for shared collection, access, and use of the system. The first test deployment of the IBEIS prototype will be at a nature conservancy in Kenya and will allow us to gather scientific data, demonstrate technical viability, and test alternative designs.

In the long term, IBEIS will be able to handle essentially unlimited number of images from multiple sources and of many species. The system will automatically analyze those images, extracting ecologically and biologically relevant information and will provide an infrastructure for accessing and analyzing it. With such a system, the science of ecology and population biology, together with the resource management, biodiversity, and conservation decisions that depend on this science, could be dramatically improved. Contributing this information and access to it would increase the public?s engagement in science and conservation, making them true citizen scientists and policy makers.











































 
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