Abstract
ABSTRACT Accurate subsurface characterization is essential in geotechnical surveys, yet traditional soil sampling techniques often suffer from sparse data coverage and limited spatial resolution. This review explores recent advancements in Intermediate Soil Properties (ISP) interpolation as a solution to these limitations. By integrating geoprocessing tools with Multidimensional Raster Data (MRD), the proposed approach enables high‐resolution, three‐dimensional modeling of soil property gradients across diverse geotechnical environments. Key findings highlight that ISP interpolation improves the fidelity of subsurface models, supports more accurate risk assessments, and enhances the safety and efficiency of construction planning. Additionally, the method reduces dependency on dense physical sampling, offering a more cost‐effective and scalable alternative. The review further discusses the comparative performance of traditional, hybrid, and machine learning‐based interpolation methods, providing insights into method selection based on data quality and project scale. It concludes with an evaluation of current limitations, including data sparsity and validation bias, and emphasizes the need for future integration with real‐time monitoring and remote sensing technologies.