Biodiversity Monitoring

Biodiversity monitoring aims to track changes in species populations and ecosystem health, often relying on labor-intensive manual data collection. Current research heavily utilizes machine learning, particularly deep neural networks (including convolutional neural networks and vision transformers), and multimodal approaches combining image, acoustic, and genetic data to automate species identification and habitat assessment from various data sources (e.g., camera traps, aerial imagery, bioacoustics). These advancements offer significant potential for improving the efficiency, scalability, and accuracy of biodiversity monitoring, informing conservation efforts and ecosystem management strategies.