Progress toward automated migratory waterfowl census using drones and deep learning-Reference-Cited by-同舟云学术

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Progress toward automated migratory waterfowl census using drones and deep learning

Published:2023-11-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 1st ACM SIGSPATIAL International Workshop on AI-driven Spatio-temporal Data Analysis for Wildlife Conservation
language:
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Author:

Converse Rowan L¹^ORCID,Lippitt Christopher D²^ORCID,Harris Grant M³^ORCID,Sesnie Steven E³^ORCID,Butler Matthew J⁴^ORCID,Stewart David R³^ORCID

Affiliation:

1. Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, New Mexico, USA

2. Geography and Environmental Studies, University of New Mexico, Albuquerque, New Mexico, USA

3. Biological Services, US Fish and Wildlife Service, Albuquerque, New Mexico, USA

4. Biological Services, US Fish and Wildlife Services, Albuquerque, New Mexico, USA

Funder

US Fish and Wildlife Service

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615893.3628757

Reference14 articles.

1. An assessment of non-breeding waterfowl surveys on National Wildlife Refuges in the Central Flyway

2. Towards estimating marine wildlife abundance using aerial surveys and deep learning with hierarchical classifications subject to error

3. A COMPARISON OF PHOTO COUNTS VERSUS VISUAL ESTIMATES FOR DETERMINING THE SIZE OF SNOW GOOSE FLOCKS

4. Population Census of a Large Common Tern Colony with a Small Unmanned Aircraft

5. Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys

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