# Teaching Computer Vision for Ecology

arXiv: https://arxiv.org/abs/2301.02211 · HTML: https://mlsanity.com/paper/2301.02211/

## Key facts

- Cited by 1 papers 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 (most cited first, 6 of 6)

- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning](https://mlsanity.com/paper/1806.11368/) (2018)
- [The Animal ID Problem: Continual Curation](https://mlsanity.com/paper/2106.10377/) (2021)

## Built on by (most cited first, 1 of 1)

- [Inteligencia Artificial para la conservaci'on y uso sostenible de la biodiversidad, una visi'on desde Colombia (Artificial Intelligence for conservation and sustainable use of biodiversity, a view from Colombia)](https://mlsanity.com/paper/2503.14543/) (2025)

## 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.