monitoring saltmarsh cordgrass using self-supervised learning

Monitoring Spartina Alterniflora Using Self-Supervised Learning


Led by researchers from Duke University and Duke Kunshan University, a team of students will embark on an interdisciplinary research journey to explore the dynamic intersection of environmental science and machine learning, engaging in the recognition of wetland plant species through the analysis of satellite image time series. Students will employ self-supervised learning based on phenological features to identify coastal wetland plant species and monitor the distribution of saltmarsh cordgrass (Spartina Alterniflora), which is a native species threatened by climate change along the Atlantic coast of the United States and has extensively invaded coastal China in the past decades. The findings have the potential to inform evidence-based conservation strategies, contributing to the preservation of coastal wetlands.

Project Lead: Wenhong Li, Ding Ma

Project Manager: Keqi He

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