Data

Mega-Efficient Wildlife Classifier (MEWC) Case Study

University of Tasmania, Australia
Barry Brook ; Jessie Buettel
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://data.utas.edu.au/metadata/3a2d9dcf-f8fa-4514-aab0-b9d36f5a1983&rft.title=Mega-Efficient Wildlife Classifier (MEWC) Case Study&rft.identifier=https://data.utas.edu.au/metadata/3a2d9dcf-f8fa-4514-aab0-b9d36f5a1983&rft.publisher=University of Tasmania, Australia&rft.description=We provide training, test and inference data for the Mega-Efficient Wildlife Classifier (MEWC) workflow, using an example camera-trap dataset collected and curated by Barry Brook and Jessie Buettel, from Tasmania, Australia. These images are drawn from a variety of environmental contexts (dry and wet temperate eucalypt forest, woodland, and grasslands) using white-, infra-red (IR) and no-glow flash types from Cuddeback, Reconyx, Swift and Bushnell cameras. The following species or aggregated classes are represented in the dataset: Tasmanian Pademelon (Thylogale billardierii), Bennetts Wallaby (Notamacropus rufogriseus), Tasmanian Devil (Sarcophilus harrisii), Feral Cat (Felis catus), Bare-nosed Wombat (Vombatus ursinus), Brushtail Possum (Trichosurus vulpecula), Fallow Deer (Dama dama), Southern Brown Bandicoot (Isoodon obesulus), Currawong (Black: Strepera fuliginosa, Grey: S. versicolor) and Bronzewing (Brush: Phaps elegans, Common: P. chalcoptera). The latter two classes are birds, each of which consist of an aggregation of two species within a genus; the former eight are mammals. For implementing the classifier training, we provide 4,000 train and 1,000 test images for each of 10 different classes, for a total of 50,000 expert-labelled snips (each sized at 600- × 600-pixel, after being extracted from their original images using the MEWC-Snip tool). For demonstrating the detection, inference, and post-processing pipelines (EXIF writing and image sorting), we provide a sequence of 100 images for each of four field cameras that were not used in training, located on the lead author’s rural property in southern Tasmania: C3: IR flash, C7: white flash, C15: no-glow flash and C21: inbuilt IR flash. Other than the target wildlife, these images include some representations of blank images, humans (the lead author), and vehicles (trail-bike motorcycle), to demonstrate the four broad classes designated by the MegaDetector prior to classification on the animal images.&rft.creator=Barry Brook &rft.creator=Jessie Buettel &rft.date=2023&rft.relation=10.1071/WR21056&rft.relation=10.3390/ani11061691&rft.relation=10.1098/rspb.2022.0521&rft.relation=10.1111/ele.13703&rft.relation=10.2326/osj.21.3&rft.coverage=northlimit=-39.4581977454144; southlimit=-43.7599365319079; westlimit=143.668212890625; eastlimit=148.590087890625; projection=WGS84&rft_rights=Attribution - NonCommercial(BY - NC) http://creativecommons.org/licenses/by-nc/4.0/&rft_subject=Wildlife and habitat management&rft_subject=Environmental management&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Deep learning&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Assessment and management of terrestrial ecosystems&rft_subject=Terrestrial systems and management&rft_subject=ENVIRONMENTAL MANAGEMENT&rft_subject=Terrestrial biodiversity&rft_subject=wildlife&rft_subject=camera trap&rft_subject=Tasmania&rft_subject=detection&rft_subject=classification&rft_subject=inference&rft_subject=field site&rft_subject=ecological community&rft_subject=mammal&rft_subject=bird&rft_subject=deep learning&rft_subject=artificial intelligence&rft.type=dataset&rft.language=English Access the data
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We provide training, test and inference data for the Mega-Efficient Wildlife Classifier (MEWC) workflow, using an example camera-trap dataset collected and curated by Barry Brook and Jessie Buettel, from Tasmania, Australia. These images are drawn from a variety of environmental contexts (dry and wet temperate eucalypt forest, woodland, and grasslands) using white-, infra-red (IR) and no-glow flash types from Cuddeback, Reconyx, Swift and Bushnell cameras. The following species or aggregated classes are represented in the dataset: Tasmanian Pademelon (Thylogale billardierii), Bennetts Wallaby (Notamacropus rufogriseus), Tasmanian Devil (Sarcophilus harrisii), Feral Cat (Felis catus), Bare-nosed Wombat (Vombatus ursinus), Brushtail Possum (Trichosurus vulpecula), Fallow Deer (Dama dama), Southern Brown Bandicoot (Isoodon obesulus), Currawong (Black: Strepera fuliginosa, Grey: S. versicolor) and Bronzewing (Brush: Phaps elegans, Common: P. chalcoptera). The latter two classes are birds, each of which consist of an aggregation of two species within a genus; the former eight are mammals. For implementing the classifier training, we provide 4,000 train and 1,000 test images for each of 10 different classes, for a total of 50,000 expert-labelled snips (each sized at 600- × 600-pixel, after being extracted from their original images using the MEWC-Snip tool). For demonstrating the detection, inference, and post-processing pipelines (EXIF writing and image sorting), we provide a sequence of 100 images for each of four field cameras that were not used in training, located on the lead author’s rural property in southern Tasmania: C3: IR flash, C7: white flash, C15: no-glow flash and C21: inbuilt IR flash. Other than the target wildlife, these images include some representations of blank images, humans (the lead author), and vehicles (trail-bike motorcycle), to demonstrate the four broad classes designated by the MegaDetector prior to classification on the animal images.

Data time period: 2018 to 2023

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Subjects
Assessment and Management of Terrestrial Ecosystems | Deep Learning | Environmental Management | Environmental Sciences | Environmental Management | Information and Computing Sciences | Machine Learning | Tasmania | Terrestrial Biodiversity | Terrestrial Systems and Management | Wildlife and Habitat Management | artificial intelligence | bird | camera trap | classification | deep learning | detection | ecological community | field site | inference | mammal | wildlife |

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