This paper presents a framework for validating global above-ground biomass (AGB) maps by comparing them to National Forest Inventories and research plots, accounting for plot uncertainties like measurement and sampling errors. The framework assesses map bias using machine learning and spatial statistics. Validation of four AGB maps reveals regional biases, spatial error correlations, and improvement trends over time. Bias-adjusted AGB estimates align better with FAO Forest Resources Assessment data. This approach supports consistent accuracy evaluation, aiding future map refinement and applications.