Assessing urban resilience requires accurate data on building age as a temporal proxy associated with structural vulnerability, yet persistent cloud cover and rapid development constrain data availability in tropical and subtropical regions. We propose a computationally efficient framework that prioritizes annual data integrity over monthly granularity to map building construction years. By combining annual cloud-free Landsat NDVI (Normalized Difference Vegetation Index) composites with open-source building footprints, the framework utilizes a Temporal Template Matching (TTM) algorithm to detect the distinct “vegetation-to-built” transition signal. Evaluated across two dynamic and heavily cloud-contaminated metropolitan areas—Shenzhen, China, and Hanoi, Vietnam—this approach achieves a high producer’s accuracy; furthermore, in Shenzhen, where a monthly comparative analysis was conducted, it outperforms a noise-sensitive monthly LandTrendr-based baseline by a factor of nearly 1.8. Our findings demonstrate that under persistent cloud contamination, a coarser but consistent annual composite provides a more reliable signal than finer-grained alternatives. This scalable methodology generates critical building-age datasets, offering foundational structural intelligence for potential inputs into seismic risk modeling and resilient urban planning in rapid-growth and resource-constrained regions.

Clarifying the driving mechanisms of mangrove dynamics is essential for effective ecosystem management, sustaining key ecosystem services and supporting relevant Sustainable Development Goals. However, under the ongoing coupled influences of climate change and bidirectional human interventions, existing studies remain limited in integrating cross-system processes and disentangling complex relationships among multiple drivers. To address these challenges, we develop a Land-Sea Coupled Framework (LSCF) that explicitly links terrestrial and marine systems through riverine transport processes and represents key land-sea interaction processes using quantifiable variables. By integrating intelligent models and post-hoc interpretation techniques, the framework enables the identification of key drivers, as well as the characterization of their nonlinear effects and interactions. China's mangroves are used as a representative case to operationalize and demonstrate the framework. Results reveal a hierarchical structure in the driving mechanisms of mangrove dynamics. Specifically, land-use/cover change acts as the dominant driver, exhibiting increasingly nonlinear effects over time and significantly modulated by tidal range. Climatic drivers primarily function as indirect modulators, with stronger influences during contraction, whereas hydrogeomorphic features broadly shape ecosystem response patterns. Moreover, the co-occurrence of multiple climatic stressors may generate synergistic effects, thereby increasing the risk of mangrove contraction and highlighting the potential threat of compound climate extremes. Overall, the LSCF provides a transferable analytical framework for elucidating complex mechanisms of mangrove dynamics, and the resulting regional-scale insights can inform adaptive management and policy-making at local scales.