Teaching Computer Vision for Ecology
- URL: http://arxiv.org/abs/2301.02211v1
- Date: Thu, 5 Jan 2023 18:30:17 GMT
- Title: Teaching Computer Vision for Ecology
- Authors: Elijah Cole, Suzanne Stathatos, Bj\"orn L\"utjens, Tarun Sharma,
Justin Kay, Jason Parham, Benjamin Kellenberger, Sara Beery
- Abstract summary: Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.
Computer vision is an emerging discipline that is rarely taught to ecologists.
This document is intended for computer scientists who teach computer vision across disciplines, but it may also be useful to ecologists or other domain experts who are learning to use computer vision themselves.
- Score: 7.461945508026928
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer vision can accelerate ecology research by automating the analysis of
raw imagery from sensors like camera traps, drones, and satellites. However,
computer vision is an emerging discipline that is rarely taught to ecologists.
This work discusses our experience teaching a diverse group of ecologists to
prototype and evaluate computer vision systems in the context of an intensive
hands-on summer workshop. We explain the workshop structure, discuss common
challenges, and propose best practices. This document is intended for computer
scientists who teach computer vision across disciplines, but it may also be
useful to ecologists or other domain experts who are learning to use computer
vision themselves.
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