There has been a marked increase in availability of high-resolution remotely-sensed datasets over the past eight years. The ability to efficiently extract accurate and meaningful land-cover information from these datasets is crucial if the full potential of these datasets is to be harnessed. Land-cover datasets, particularly high-resolution ones, must be statistically accurate and depict a realistic representation of the landscape if they are to be used by decision makers and trusted by the general public. Furthermore, if such datasets are to be accessible and relevant, mechanisms must exist that facilitate cost-effective and timely production. Object-based image analysis (OBIA) techniques offer the greatest potential for generating accurate and meaningful land-cover datasets in an efficient manner. They overcome the limitations of traditional pixel-based classification methods by incorporating not only spectral data but also spatial and contextual information, and they offer substantial efficiency gains compared to manual interpretation. Drawing on our experience in applying OBIA techniques to high-resolution data, we believe any automated approach to land-cover mapping must: 1) effectively replicate the human image analyst; 2) incorporate datasets from multiple sources; and 3) be capable of processing large datasets. To meet this functionality, an operational OBIA system should: 1) employ expert systems; 2) support vector and raster datasets; and 3) leverage enterprise computing architecture.