Andrew Michael Durso

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, species identification and inventory is a difficult and costly task, requiring large-scale automated approaches. The LifeCLEF lab has been promoting and evaluating advances in this domain since 2011 through the organization of multi-year challenges. The 2024 edition presented in this article proposes five data-driven challenges as a continuation of this effort: (i) BirdCLEF: bird species recognition in audio soundscapes, (ii)FungiCLEF: fungi recognition beyond 0-1 cost, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) PlantCLEF: Multi-species identification in vegetation plot images, and (v) SnakeCLEF: snake recognition in medically important scenarios.

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.

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: Snake Species Identification in Medically Important scenarios, and (v)  FungiCLEF: Fungi recognition from images and metadata.

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, and (iv) SnakeCLEF: Automatic Snake Species Identification with Country-Level Focus.

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data and (iv) SnakeCLEF: image-based snake identification.

A robust and accurate AI-driven system as an assistance tool for snake species identification has vast potential to help lower deaths and disabilities caused by snakebites. With that in mind, we prepared the SnakeCLEF 2021: Automatic Snake Species Identification Challenge with Country-Level Focus, designed to provide an evaluation platform that can help track the performance of end-to-end AI-driven snake species recognition systems with a focus on overall country-wise performance. We have provided 386,006 photographs of 772 snake species collected in 188 countries and country-species presence mapping for the challenge. In this paper, we report 1) a description of the provided data, 2) evaluation methodology and principles, 3) an overview of the systems submitted by the participating teams, and 4) a discussion of the obtained results.

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2020 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data, and (iv) SnakeCLEF: snake identification based on image and geographic location