mlsanityData to 14 Oct 2026

Teaching Computer Vision for Ecology

arXiv 2301.02211 ↗First posted 5 Jan 2023cs.CY cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 5 Jan 2023.

Builds on

6 of 44 references.

References · 6

  1. 2019Tackling Climate Change with Machine Learningcited by 265
  2. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 130
  3. 2018Troubling Trends in Machine Learning Scholarshipcited by 108
  4. 2019Efficient Pipeline for Camera Trap Image Reviewcited by 79
  5. 2018Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learningcited by 28
  6. 2021The Animal ID Problem: Continual Curationcited by 5

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