Processing massive archives of satellite imagery has historically paralyzed traditional desktop-based remote sensing laboratories. The sheer volume of computationally heavy tasks-from bulk atmospheric correction to long-term radiometric calibration-frequently exceeds the hardware capacity of local campus networks and student laptops. To bypass these severe limitations, this study presents a dual-cloud pedagogical architecture that integrates Google Earth Engine (GEE) and Alibaba's AI Earth. This hybrid framework allows students to instantly access petabytes of analysis-ready data while maintaining low-latency processing for complex modelling via domestic servers. We operationalized this setup through four core practical modules: urbanization monitoring, urban heat island analysis, nighttime light assessment, and AI-driven road extraction. By entirely eliminating the overhead of raw data management and environment configuration, students can finally redirect their cognitive focus toward the actual physics and algorithmic logic of remote sensing—such as parameterizing radiative transfer equations and interpreting radiometric time-series. Furthermore, in light of AI Earth's recent policy shift in March 2026, which heavily restricts free access for educational usage, we critically evaluate the long-term sustainability of this curriculum. To maintain unhindered access to cloud-native geoprocessing, our future instructional designs will assess alternative localized platforms and open-source AI frameworks, ensuring the uninterrupted evolution of rigorous Earth observation education.