Biodiversity Information Science and Standards : Conference Abstract
|
Corresponding author: David Thau (dave.thau@wwfus.org)
Received: 12 Jul 2019 | Published: 17 Jul 2019
© 2019 David Thau, Jorge A. Ahumada, Tanya Birch, Eric Fegraus, Nicole Flores, Walter Jetz, Roland Kays, Margaret Kinnaird, Sayali Kulkarni, Arnaud Lyet, Timothy G. O’Brien, Jonathan Palmer, Stephanie Schuttler, Michelle Duong, Ruth Oliver, Jennifer Y. Zhao, William J. McShea
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Thau D, Ahumada JA, Birch T, Fegraus E, Flores N, Jetz W, Kays R, Kinnaird M, Kulkarni S, Lyet A, O’Brien TG, Palmer J, Schuttler S, Duong M, Oliver R, Zhao JY, McShea WJ (2019) Artificial Intelligence's Role in Global Camera Trap Data Management and Analytics via Wildlife Insights. Biodiversity Information Science and Standards 3: e38233. https://doi.org/10.3897/biss.3.38233
|
|
Camera traps have existed since the 1890s (
Wildlife Insights attempts to overcome these hurdles by providing camera trap data upload, management, and analysis services. It provides the world’s largest database of camera trap images by bringing together the camera trapping efforts of several the world’s largest conservation and research organizations, and it is open to future contributors. Artificial Intelligence-driven services sit at the heart of the platform. New camera trap data uploads are automatically analyzed to differentiate between images with people, non-human animals, and no animals. The images with non-human animals are further analyzed to detect specific species. The proposed labels are sent back to the submitter for review and then uploaded to the database. All uploaded images, unless specifically embargoed, are immediately available for analysis by all users of the system. A selection of tools are provided to support analyses of global biodiversity.
This presentation will describe Wildlife Insights and its AI implementation in detail, contextualized by case studies using analyses of the data currently stored on the platform. Challenges around integrating camera trap data within the platform and with other external services that work with the platform will also be discussed. The talk will end with some thoughts about future directions for the AI services, especially with regards to integration with related platforms.
artificial intelligence, species identification, computer vision, big data
David Thau
Biodiversity_Next 2019