Abstract: The Bayesian theory provides an effective tool to properly characterize the spatial variability of soil properties and quantify the effect of site investigation data (e.g., undrained shear strength data) on reliability of slope stability. However, the site investigation data sequentially appears at different spatial locations of a slope, and the model to characterize the spatially varying soil properties (e.g., random field model) usually involves a great number of uncertain parameters. These pose a great computational challenge for Bayesian updating of slope reliability considering spatially varying soil properties. A collaborative reliability updating approach for the slope stability with spatially varying soil properties considering different site investigation data is proposed. It first makes use of the Bayesian updating with structural reliability methods (BUS) to simulate random fields and perform slope stability analyses, and then employs the rejection sampling principle and collaborative analysis to characterize the spatially varying soil properties and update the reliability of slope stability considering different test data. As the site investigation data spatially appears within a slope, repeated simulations of conditional random fields and a significant number of slope stability analyses are avoided. Moreover, the combination of the BUS makes it possible for efficient slope reliability updating using the Bayesian analysis that involves high-dimensional model parameters. A single-layered soil slope with a non-stationary random field is employed to demonstrate the effectiveness and validity of the proposed approach. It is shown that the proposed approach provides an effective tool for dynamic characterization of spatial variability of soils and real-time reliability updating of slope stability under different site investigation data.