A global validation database that can be used to assess the accuracy of multiple global and regional land-cover maps would yield significant cost savings and enhance comparisons of accuracy of different maps. Because the global validation database should expand over time as new validation data are contributed, the sampling design must be constructed so that it is simple to increase the sample size from a specific region (e.g. a continent or country) or from targeted land-cover classes to improve standard errors of the accuracy estimates. Stratified random sampling provides the desired adaptability to augment a sample to address regional or class-specific accuracy objectives. The proposed global validation database will be initiated from a baseline global stratified sample and then this baseline sample will be augmented to address accuracy objectives related to a specific map or region. The strata are constructed from a modified Köppen climate classification and population density. The theory and formulas for estimating accuracy from the combined baseline and augmented stratified samples are presented, and an example application is provided in which the regional accuracy of a land-cover map is assessed. The stratification used for the baseline global sample is retained when the augmented sample is subsequently selected. Alternatively, it is possible to ‘restratify’ the design so that the land-cover classes of a particular map are used as strata when selecting an augmented sample. The protocol for restratifying the design is presented, but the complexity of this option makes it less practical than retaining the initial strata when selecting an augmented sample.