Construction and Integration of Image Control Point, Interpretation Sample, and Spectral Information Databases for Megacity Management

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. 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. 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.