Habitat Classification

Habitat classification aims to automatically categorize and map different environments, crucial for biodiversity monitoring and conservation. Current research focuses on improving the accuracy of habitat models using diverse data sources (e.g., satellite imagery, sensor data, citizen science observations) and advanced machine learning techniques, including convolutional neural networks (CNNs), vision transformers (ViTs), and active learning strategies to address class imbalances in datasets. These advancements are improving the efficiency and accuracy of habitat mapping, informing conservation efforts, and enabling more effective monitoring of species and ecosystems.