Computer vision is a field of Artificial Intelligence that aims to teach machines to "see" and interpret images in the way humans do. This has led to important technological advancements in autonomous systems such as robotics and self-driving vehicles. In this way, Biodiversity Conservation also benefits from advances in computer vision, as it enables the analysis and interpretation of complex biological data extracted from images from various sources with greater efficiency and accuracy. Learning about computer vision provides professionals with key tools to advance research as well as to enhance understanding and knowledge about species and biodiversity conservation areas.

This course focuses on understanding the basic concepts of computer vision from the perspective of camera trap usage, as this is one of the most important applications of computer vision in biodiversity conservation. It covers common tasks involved in these projects, such as preprocessing, feature extraction, segmentation, and object detection and recognition, for the subsequent analysis and interpretation of the data obtained from images. This course does not cover tasks such as designing camera trap systems, image acquisition, or administrative aspects of such systems.

This course is a response to the needs for training in various areas of Data Science identified by the Data Science Network for the Conservation of Mesoamerican Biodiversity (Redbioma). It is aimed at professionals who work in activities related to biodiversity conservation, and therefore focuses on problem-solving and the development of knowledge and skills in image processing and analysis through the use of tools, basic programming based on image processing libraries in Python, and the use of pre-trained Machine Learning models applied to image collections available in public repositories.