This article emphasizes the innovative integration of spatial data, remote sensing, machine learning, and community science to enhance adaptive management strategies aimed at supporting coastal resilience. By advancing the use of spatial analysis, this study addresses the pressing challenges posed by sea-level rise and shoreline erosion. The research focuses on mapping key shoreline features and coastal habitats in Virginia, USA, utilizing photo-interpretation and machine-learning techniques to update shoreline inventories efficiently and accurately. This novel approach not only improves the VIMS Shoreline Management Model (SMM) but also incorporates community science through a mobile application, allowing local residents to contribute valuable data on shoreline conditions and coastal habitats. By fostering community engagement and leveraging real-time information, decision-makers are better equipped to implement adaptive coastal management practices. Ultimately, this study highlights the critical importance of comprehensive spatial data and community involvement in developing effective strategies for enhancing coastal resilience in the face of climate change.