Biodiversity monitoring using machine learning and AI-based approaches is becoming
increasingly popular. It allows for providing detailed information on species distribution and …

Highlights•Human-in-the-loop (HITL) design is critical for mitigating biases in AI-led
decisions.•HITL AI can harness people's behavioural insights for contextualised climate …

The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological
knowledge and conservation efforts. To fill this gap, Species Distribution Models (SDMs) …

The BirdCLEF 2023 challenge focused on bird species classification in a dataset of Kenyan
soundscape recordings. Kenya is home to over 1,000 species of birds, covering a wide …

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 …

We present TaxaBind, a unified embedding space for characterizing any species of interest.
TaxaBind is a multimodal embedding space across six modalities: ground-level images of …

Monitoring of bird species plays a vital role in understanding biodiversity trends, as birds
serve as reliable indicators of ecological change. Traditional observer-based bird surveys …

This paper presents a deep learning approach for recognizing bird species in soundscape
recordings using Convolutional Neural Networks (CNNs). The proposed method extends …

Understanding the geographical distribution of plant species is useful in many scenarios
related to biodiversity management and conservation. By associating plant species …

The SnakeCLEF2024 competition aims to develop an advanced algorithm capable of
automatically identifying snake species from images. Accurate identification of snake …