Sustainable development goals such as food security, high-quality habitat construction, biodiversity conservation, planetary health, and the understanding, modeling, and management of the Earth system urgently require multi-scale, long time series, high-accuracy, and consistent remote sensing observation datasets and mapping products with flexible classification systems to meet user needs. However, due to technical and cost constraints, it is difficult for conventional remote sensing satellites to provide observations with high spatial resolution, high temporal frequency, and high quality at the same time. The existing mapping and inversion schemes are mostly for a single sensor, making it difficult to fully exploit and jointly utilize the information potential of multi-source heterogeneous remote sensing big data, resulting in limited observation periods and resolutions, low spatial and temporal consistency and comparability. Therefore, new technical paradigms are urgently needed in the field of remote sensing. In this paper, based on advanced technologies, including cloud computing, artificial intelligence, virtual constellation, spatio-temporal fusion reconstruction, an intelligent mapping framework is proposed for remote sensing big data. The framework is user-driven and problem-driven, which can significantly improve the current situation that remote sensing data products can hardly meet users’ diversified and high-precision surface monitoring needs in agriculture and forestry management, national monitoring, ecological environment protection, disaster prevention and mitigation, urban planning, etc. Under this framework’s guidance, we built an online real-time, automated, serverless, end-to-end remote sensing big data production chain and parallel mapping system based on Amazon Web Services (AWS) high-performance, elastic, and scalable distributed computing resources. We produced the first set of 21st century daily Seamless Data Cube (SDC) and seasonal to annual land cover and land use mapping products of China. Integrating Landsat and MODIS satellite as a virtual constellation, through multi-source spatio-temporal data fusion and reconstruction technology, the daily SDC, cloud-free, high-precision reflectance product, is developed. As Analysis Ready Data (ARD), it lays the foundation for high-precision quantitative remote sensing inversion and mapping. Based on SDC, we developed the seasonal to annual mapping product with multiple multi-level land cover and land use classification systems, whose mean annual accuracy exceeds 80%. The main mapping pipeline includes migrating the all-season sample set based on stable classification theory with limited samples, optimizing and ensembling multiple classifiers by Automatic Machine Learning (AutoML) strategies, and using change detection and post-processing techniques to achieve consistency. The two sets of products demonstrate the feasibility and effectiveness of the intelligent remote sensing mapping framework proposed in this paper. We will continue to improve and develop the framework with an open and flexible concept to provide new ideas to promote remote sensing development in China.