Bamboo exhibits a rapid growth rate and mechanical properties comparable to those of wood, making it an attractive material for sustainable construction. Its hierarchical structure results in significant anisotropy and spatial heterogeneity [1,2], yet a comprehensive mesoscale characterization remains limited. While previous studies have investigated the mechanics of vascular bundles [3,4], a deeper understanding of their structural and mechanical role is still needed. This contribution presents computational approaches for analyzing the statistically inhomogeneous mesostructure and linear elastic properties of Phyllostachys edulis (Moso bamboo). The mesostructure of the culm wall is characterized via advanced computational image processing techniques, including the segmentation of cross-sectional images, AI-driven identification of individual vascular bundles, and statistical analysis to extract structural characteristics. Further- more, according to the measured characteristics a representative volume element (RVE) of the mesostructure can be reconstructed for any radial position within the culm wall. The generated RVE reflects the structural characteristics of a given radial position without considering the gradient of the characteristics and therefore is statistically homogeneous. To predict the culm wall’s macroscopic stiffness, a multiscale homogenization model is developed using analytical and numerical methods. First, mean-field models compute the anisotropic, linear elastic properties of the matrix and fiber bundles, considering their heterogeneity at microsopic scale. In a next step, the macroscopic properties are derived by an FFT-based homogenization model using the mesostructural RVEs and local properties as input. This approach enables the study of the material anisotropy and inhomogeneity and offers key insights into mesoscopic phenomena driving bamboo’s mechanical behavior. Furthermore, aspects of the thermoviscoelastic properties of bamboo are discussed.