BioDetect
Deep Learning for Biodiversity Detection and Classification
Abstract
Monitoring biodiversity is a fundamental challenge in protecting the natural world. Human-based biodiversity surveys are expensive, logistically difficult, and time-consuming. To increase the scale of data collection, ecology has increasingly turned to remote image capture. However, the challenge of turning enormous pools of imagery into information on species abundance and behaviour has severely restricted the growth of automated monitoring. Bringing the recent progress in image-based artificial intelligence and computer vision to the wider ecological community depends on close collaborations between domain and data scientists. This collaboration will broaden the use of computer vision in ecology, as well as provide opportunities for ground- breaking research in computer vision. Building from existing datasets in tropical ecosystems and Swiss aquatic environments, we will develop, build, and distribute computer vision tools for biodiversity detection and identification. The data and algorithms developed for this proposal will be openly shared with the ecology and computer vision communities to foster growth between the disciplines.
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Michele received a Ph.D. in Environmental Sciences from the University of Lausanne (Switzerland) in 2013. He was then a visiting postdoc in the CALVIN group, Institute of Perception, Action and Behaviour of the School of Informatics at the University of Edinburgh, Scotland (2014-2016). He then joined the Multimodal Remote Sensing and the Geocomputation groups at the Geography department of the University of Zurich, Switzerland (2016-2017). His main research activities were at the interface of computer vision, machine and deep learning for the extraction of information from aerial photos, satellite optical images and geospatial data in general.
description
Problem:
The project aims at developing and adapting robust computer vision methods for biodiversity monitoring. The methods developed in the framework of this project will be tested on two different case studies: hummingbird detection and recognition in camera trap videos and inference of species traits from images of macroinvertebrates in Swiss rivers. For both case studies results from computer vision models will be used to formulate and support hypotheses about interaction of species with their environment (e.g. hummingbirds and plant species).
Proposed approach:
We will deal with methods able to perform fine-grained classification of species by integrating domain-knowledge, such as information about the ecosystems and geographical settings. In parallel, we will work on methods able to infer species traits from image data, thus taking one step further from plain fine-grained classification.
Impact:
The impact of the project will reach beyond computer vision, but potentially enabling accurate biodiversity monitoring based on video and image data. The project will make the contributions openly available so that any research in animal biodiversity could profit.
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Additionnal resources
Bibliography
- Graham, C.H. and Weinstein, B. G. Towards a predictive model of species interaction betadiversity. Ecology Letters, 2018, 21: 1299-1310.
- Weinstein, B.G. A computer vision for animal ecology. J. Anim. Ecol. 2018, 87, 533–545.
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