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.