Accelerating ecosystem monitoring through computer vision with deep metric learning

Feature extraction from environmental observation data based on deep learning models has made significant progress. However, the current methods may not be optimal because of the increasing volume of data, complexity of data characteristics, and labeled data limitations. In this study, we focused on deep metric learning as a new application for environmental observation data to overcome these challenges. The extraction of features such as patterns and changes from large and complex environmental observation data using a deep metric learning approach may provide new opportunities for monitoring ecosystems experiencing unprecedented loads from climate change and human activities. We expect that deep metric learning will be a powerful tool for various ecosystem monitoring systems, from remote sensing of wide-area data to ecological data obtained through field surveys. DOI https://doi.org/10.32942/X2K031 Subjects Biodiversity, Life Sciences

ecosystem monitoring, deep metric learning, Few-Shot Learning, Zero-Shot Learning, remote sensing, field observation data Dates Published: 2024-07-19 18:04 License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: Not applicable Language: English