A general deep learning model for bird detection in high‐resolution airborne imagery-Reference-Cited by-同舟云学术

A general deep learning model for bird detection in high‐resolution airborne imagery

Published:2022-08-10 Issue:8 Volume:32 Page:
ISSN:1051-0761
Container-title:Ecological Applications
language:en
Short-container-title:Ecological Applications

Author:

Weinstein Ben G.¹^ORCID,Garner Lindsey¹,Saccomanno Vienna R.²,Steinkraus Ashley¹,Ortega Andrew³,Brush Kristen⁴,Yenni Glenda¹,McKellar Ann E.⁵,Converse Rowan⁶,Lippitt Christopher D.⁶,Wegmann Alex²,Holmes Nick D.²,Edney Alice J.⁷^ORCID,Hart Tom⁷,Jessopp Mark J.⁸,Clarke Rohan H.⁹,Marchowski Dominik¹⁰,Senyondo Henry¹,Dotson Ryan¹¹,White Ethan P.¹,Frederick Peter¹,Ernest S. K. Morgan¹

Affiliation:

1. Department of Wildlife Ecology and Conservation University of Florida Gainesville Florida USA

2. California Oceans Program The Nature Conservancy Sacramento California USA

3. Geomatics Program University of Florida Gainesville Florida USA

4. Montana State University Bozeman Montana USA

5. Environment and Climate Change Canada Saskatoon Saskatchewan Canada

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

7. Department of Zoology University of Oxford Oxford UK

8. School of Biological, Earth and Environmental Sciences University College Cork Cork Ireland

9. School of Biological Sciences Monash University Melbourne Victoria Australia

10. Ornithological Station Museum and Institute of Zoology, Polish Academy of Sciences Gdańsk Poland

11. Quantaero Nevada Reno USA

Funder

Gordon and Betty Moore Foundation

Microsoft Research

Publisher

Wiley

Subject

Ecology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/eap.2694

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