Abstract
Developing an effective automatic system for snake species identification has significant importance for biodiversity, conservation, and global health. Snakebites result in over half a million deaths and disabilities worldwide each year, highlighting the urgent need for a system to enhance eco-epidemiological data and improve treatment outcomes, especially in remote regions that lack the necessary expertise and data but have high snake diversity and a high incidence of snakebites. The SnakeCLEF challenge provide an evaluation ground that helps track the performance of AI-driven methods for snake species recognition systems on a global scale. The fourth edition of the SnakeCLEF challenge focuses on (i) evaluation of gradual improvements in automatic snake species identification, (ii) testing worldwide generalization on two specific scenarios, i.e., India and Central America, and (iii) evaluation with uneven costs for different errors, such as mistaking a venomous snake for a harmless one. This paper showcases the vital role of a robust automatic identification system for snakes, particularly in regions with limited resources, and highlights the potential impact on biodiversity conservation and global health outcomes. We report (i) a comprehensive description of the provided data, (ii) an evaluation methodology, (iii) an overview of the submitted methods, and (iv) perspectives derived from the achieved results. © 2023 Copyright for this paper by its authors.
