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Scaling Biodiversity Monitoring for the Data Age

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Sara BeeryAuthors Info & Claims

XRDS: Crossroads, The ACM Magazine for Students, Volume 27, Issue 4

Pages 14 - 18

https://doi.org/10.1145/3466857

Published: 24 June 2021 Publication History

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Abstract

Technological advances have made it possible to collect massive amounts of biodiversity data. How can analysis efforts keep up?

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References

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Darras KBalle MXu WYan YZakka VToledo‐Hernández MSheng DLin WZhang BLan ZFupeng LWanger T(2024)Eyes on nature: Embedded vision cameras for terrestrial biodiversity monitoringMethods in Ecology and Evolution10.1111/2041-210X.1443615:12(2262-2275)Online publication date: 28-Oct-2024
https://doi.org/10.1111/2041-210X.14436
Jain ACunha FBunsen MCañas JPasi LPinoy NHelsing FRusso JBotham MSabourin MFréchette JAnctil ALopez YNavarro EPimentel FZamora ASilva JGagnon JAugust TBjerge KSegura ABélisle MBasset YMcFarland KRoy DHøye TLarrivée MRolnick D(2024)Insect Identification in the Wild: The AMI DatasetComputer Vision – ECCV 202410.1007/978-3-031-72913-3_4(55-73)Online publication date: 29-Sep-2024
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Scaling Biodiversity Monitoring for the Data Age

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Published In

cover image XRDS: Crossroads, The ACM Magazine for Students

XRDS: Crossroads, The ACM Magazine for Students Volume 27, Issue 4

Computing and Sustainability

Summer 2021

59 pages

ISSN:1528-4972

EISSN:1528-4980

DOI:10.1145/3472736

Editor:
Karan Ahuja
Carnegie Mellon University

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This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2021

Published in XRDS Volume 27, Issue 4

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Darras KBalle MXu WYan YZakka VToledo‐Hernández MSheng DLin WZhang BLan ZFupeng LWanger T(2024)Eyes on nature: Embedded vision cameras for terrestrial biodiversity monitoringMethods in Ecology and Evolution10.1111/2041-210X.1443615:12(2262-2275)Online publication date: 28-Oct-2024
https://doi.org/10.1111/2041-210X.14436
Jain ACunha FBunsen MCañas JPasi LPinoy NHelsing FRusso JBotham MSabourin MFréchette JAnctil ALopez YNavarro EPimentel FZamora ASilva JGagnon JAugust TBjerge KSegura ABélisle MBasset YMcFarland KRoy DHøye TLarrivée MRolnick D(2024)Insect Identification in the Wild: The AMI DatasetComputer Vision – ECCV 202410.1007/978-3-031-72913-3_4(55-73)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72913-3_4
Tkachenko N(2024)Opportunities for synthetic data in nature and climate financeFrontiers in Artificial Intelligence10.3389/frai.2023.11687496Online publication date: 9-Jan-2024
https://doi.org/10.3389/frai.2023.1168749
Cañas JToro-Gómez MSugai LBenítez Restrepo HRudas JPosso Bautista BToledo LDena SDomingos Ade Souza FNeckel-Oliveira Sda Rosa ACarvalho-Rocha VBernardy JSugai Jdos Santos CBastos RLlusia DUlloa J(2023)A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoringScientific Data10.1038/s41597-023-02666-210:1Online publication date: 6-Nov-2023
https://doi.org/10.1038/s41597-023-02666-2
Davis C(2023)The herbarium of the futureTrends in Ecology & Evolution10.1016/j.tree.2022.11.01538:5(412-423)Online publication date: May-2023
https://doi.org/10.1016/j.tree.2022.11.015
Shimotoku DYuan TParajuli LKobayashi H(2022)Participatory Sensing Platform Concept for Wildlife Animals in the Himalaya Region, NepalDistributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity10.1007/978-3-031-05431-0_6(87-98)Online publication date: 26-Jun-2022
https://dl.acm.org/doi/10.1007/978-3-031-05431-0_6

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