Economic impacts of multimodal learning in coastal zone monitoring and geodata management

Introduction Coastal zones are economically vital regions, supporting dense populations, intensive trade, and strategic infrastructure. However, their development is increasingly threatened by environmental degradation, spatial resource conflicts, and policy fragmentation. These challenges call for analytical frameworks that can jointly capture the spatial, economic, and ecological dynamics governing coastal systems. Traditional models often struggle to address this complexity, particularly overlooking spatial heterogeneity, ecological feedback mechanisms, and stochastic environmental changes. Such limitations hinder policymakers from achieving a balance between economic growth and long-term sustainability. Methods To address these issues, this study introduces a Coastal Adaptive Economic Dynamics Model (CAEDM), which integrates dynamic optimization, spatial externalities, and stochastic shocks to more accurately reflect the interplay between economic activities and environmental dynamics in coastal regions. Building on this foundation, we further propose the Resilient Coastal Economic Optimization Strategy (RCEOS) to optimize resource allocation, mitigate environmental degradation, and facilitate the spatial redistribution of economic activities, ensuring the resilience and adaptive capacity of coastal ecosystems. Results We develop CAEDM using multimodal deep learning and coupled spatiotemporal modeling, which jointly support real-time monitoring and policy simulation. Quantitative evaluations demonstrate that CAEDM achieves up to 3.5% higher accuracy and 4.2% better AUC compared to state-of-the-art models on benchmark datasets including AVSD and Coastal Tourism. Discussion This research aligns with the evolving needs of coastal zone monitoring and geodata management, offering actionable insights for enhancing long-term economic resilience and environmental sustainability in coastal areas.