Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks
| Autor: | Amy McGovern, Daniel Sheldon, Carmen Chilson, Katherine Avery, Jeffrey F. Kelly, Eli S. Bridge |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
010504 meteorology & atmospheric sciences Ecology Computer science business.industry Deep learning Supercomputer NEXRAD 010603 evolutionary biology 01 natural sciences Convolutional neural network law.invention Advice (programming) World Wide Web law Center (algebra and category theory) Artificial intelligence Computers in Earth Sciences Radar business Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences Nature and Landscape Conservation |
| Zdroj: | Remote Sensing in Ecology and Conservation. 5:20-32 |
| ISSN: | 2056-3485 |
| DOI: | 10.1002/rse2.92 |
| Popis: | The funding from the NSF-DGE-1545261 grant helped make this research possible. We thank Sandra Pletschet for her time spent collecting the roost data and Dr. Phillip Chilson for his advice on the project. Some of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). Article processing charges for this publication funded in part by the University of Oklahoma Libraries Open Access Fund. |
| Databáze: | OpenAIRE |
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