Abstract: Global mainstream remote sensing platforms for intelligent identification of geological hazards and related elements (e.g., houses, roads, and other factors potentially affected by geological hazards) generally face common challenges, such as data security and privacy concerns, cost and resource limitations, dependency on proprietary technologies, and poor flexibility. To address these challenges, this study designed and implemented an intelligent identification platform that integrates sample construction, model training, and intelligent identification. This platform achieves a breakthrough in the full-process and large-scale intelligent identification of geological hazards and related elements. It efficiently supports the multi-element, multi-scenario, and multi-model applications, significantly improving the computational accuracy and efficiency while also enhancing technological autonomy. This study provides a replicable and generalizable paradigm for developing comprehensive remote sensing platforms for intelligent identification of natural resources.