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This article presents a study of Gaussian process (GP) models applied to the problems of modeling and data fusion in the context of large-scale terrain modeling. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. These attributes are considered essential to support most field robotics applications, including autonomous mining. GP regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. GP approximation methods are introduced to enable the application of the proposed techniques to large data sets. To obtain a comprehensive model of complex terrain, typically, multiple sensory modalities and multiple data sets are required. The GP modeling approach is consequently extended to fuse multiple, multimodal data sets to obtain a best estimate of the elevation given the individual data sets. Two different GP-based concepts are applied to perform data fusion-heteroscedastic GPs and dependent GPs (DGPs). Thus, this article presents a report on an ongoing study of the use of GPs and several GPbased concepts to the problem of large-scale terrain modeling in the context of mining automation.