With the rapid advancement of satellite, aerial, and UAV platforms, the daily volume of remote sensing data collected over megacities has grown exponentially. However, only a limited portion of this data can be transformed into usable products in time. Current production workflows remain lengthy and poorly automated, which fails to meet the increasing demand for high-precision and high-timeliness remote sensing products in city management, environmental monitoring, and emergency response.</p> <p>To address this gap, this study proposes the construction of an standardized, efficient and reusable foundational database system consisting of three key components: image control point database, interpretation sample database, and spectral information database. The image control point database establishes a unified geometric reference for multi-source data; The interpretation sample database provides large-scale, high-quality labeled data for deep learning-based image analysis; and the spectral database offers standardized spectral features for accurate classification and parameter inversion.</p> <p>Together, the three databases form a collaborative mechanism that links geometric accuracy, semantic understanding, and spectral consistency, thereby building a complete chain from analysis-ready data (ARD) production to rapid information extraction. Using Shanghai as a case study, this paper presents the design, construction, and collaborate applications of the three databases, demonstrating their effectiveness in supporting refined and sustainable megacity governance.