A persistent mismatch between remote sensing image acquisition and information processing capabilities poses a critical challenge for emergency monitoring. To address this issue, this study proposes an integrated framework that matches the observation requirements of different disaster types across three dimensions—scale, information, and time. Based on this framework, we design a standardized solution for remote sensing data organization, processing, and application, enabling precise grid-based localization and collective interpretation in emergency-affected areas. Validation experiments conducted on a prototype ground-based information integration and response platform, built for emergency observation with domestic satellites, demonstrate significant improvements in efficiency: the volume of processed data was reduced by approximately 99.7%, data transmission time was shortened from minutes to seconds, and regional target extraction was achieved within a minute-scale timeframe. These results confirm that the proposed solution markedly accelerates the transmission and processing of emergency remote sensing data, providing strong support for the rapid localization and extraction of emergency targets.

With the development of remote sensing technology, remote sensing has been widely applied in various fields and has become one of the important high-tech means to serve modern production and life of humanity. There is an urgent demand for ubiquitous remote sensing services that cater to the needs of hundreds of stars and hundreds of uses. However, due to the heterogeneity of remote sensing data, complexity of remote sensing technology, and diversity of application scenarios, remote sensing service technology for ubiquitous application scenarios is not yet mature. Among them, the standardization technology and application of remote sensing products have become a hot research topic. This article proposes a remote sensing information container information model, SPID system architecture model, and COGON network architecture model based on standard quantitative products. It proposes a remote sensing data application mode of "scalding, hot, warm, cool and icy" and conducts research on the design of multi-source heterogeneous remote sensing service mode for ubiquitous application scenarios, helping remote sensing applications shift from traditional professional data services to ubiquitous application services.