Abundance Estimation

Abundance estimation aims to determine the proportional representation of different components within a complex system, a crucial task across diverse scientific fields. Current research heavily utilizes machine learning, employing techniques like convolutional neural networks, generative adversarial networks, and recurrent neural networks, often integrated with other data sources (e.g., geographic, spectral, temporal) to improve accuracy and address challenges like missing data or noise. These advancements are significantly impacting various domains, from remote sensing (e.g., hyperspectral unmixing for land cover analysis) and astrophysics (e.g., determining elemental abundances in stars) to ecological monitoring (e.g., estimating wildlife populations from acoustic or camera trap data). The development of robust and efficient abundance estimation methods is driving progress in understanding complex systems and informing data-driven decision-making.