This chapter explores intelligent offline data updating techniques designed to improve the accuracy and robustness of CSI-based localization systems. It begins with an overview of adaptive data sampling strategies, highlighting the differences between traditional and intelligent updating approaches and identifying the key challenges in maintaining real-time fingerprint reliability. Next, we introduce CSI prediction models, including machine learning-based approaches, hybrid methods that combine real and predicted data, as well as techniques based on crowdsourcing and multivariate Gaussian regression. To address missing CSI data, we investigate various generative strategies, including the application of large-scale models for data generation. We also evaluate robustness enhancements achieved through data augmentation and discuss the limitations with synthetic data. In addition to prediction and augmentation strategies, we further investigate methodologies for constructing and refining CSI fingerprint databases. This includes building initial radio maps, analyzing CSI error bounds, and proposing behavior cloning techniques based on imitation learning for fine-grained radio map generation. The chapter concludes by introducing the Deep-Broad Learning (DBLG) algorithm, outlining its motivation, system architecture, the integration of a GAN model with confidence-based weighting, and experimental validation of its performance.