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      Artificial Intelligence's Role in Global Camera Trap Data Management and Analytics via Wildlife Insights

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          Abstract

          Camera traps have existed since the 1890s (Kucera and Barrett 2011), but they weren’t widely used until the introduction of commercial infrared-triggered cameras in the early 1990s (Meek et al. 2014). Since then, millions, perhaps billions of camera trap images have been collected for many reasons, biodiversity monitoring being one of the key applications. Unfortunately, although there are camera trap deployments all over the world, these operations occur in isolation, limiting the impact they could have on a global understanding of biodiversity health. Even within individual institutions, managing and analyzing multiple camera trap deployments in aggregate can be challenging. In fact, managing a single deployment of camera traps is non-trivial and important data are frequently cast aside as bycatch, left unanalyzed on decaying hard drives. 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.

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          Camera Trapping : Wildlife Management and Research

          Camera trapping in wildlife management and research is a growing global phenomenon. The technology is advancing very quickly, providing unique opportunities for collecting new biological knowledge. In order for fellow camera trap researchers and managers to share their knowledge and experience, the First International Camera Trapping Colloquium in Wildlife Management and Research was held in Sydney, Australia. Camera Trapping brings together papers from a selection of the presentations at the colloquium and provides a benchmark of the international developments and uses of camera traps for monitoring wildlife for research and management. Four major themes are presented: case studies demonstrating camera trapping for monitoring; the constraints and pitfalls of camera technologies; design standards and protocols for camera trapping surveys; and the identification, management and analyses of the myriad images that derive from camera trapping studies. The final chapter provides future directions for research using camera traps. Remarkable photographs are included, showing interesting, enlightening and entertaining images of animals 'doing their thing', making it an ideal reference for wildlife managers, conservation organisations, students and academics, pest animal researchers, private and public land managers, wildlife photographers and recreational hunters.
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            Author and article information

            Journal
            Biodiversity Information Science and Standards
            BISS
            Pensoft Publishers
            2535-0897
            July 17 2019
            July 17 2019
            : 3
            Article
            10.3897/biss.3.38233
            059465c1-b300-438b-9437-4f864d83aa45
            © 2019

            http://creativecommons.org/licenses/by/4.0/

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