Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional space. They provide a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. They also enable building of data-driven computational models of brain regions. For atlases of the cerebral cortex, additional information is required to work effectively with its particular, layered architecture and curved geometry. Although some approaches have been employed in the literature, no usable method to produce such information is openly available. To fill this gap, we describe here methods to enhance a cortical atlas with three auxiliary, voxel-wise datasets: first, a field of cortical depth; second, a field of local orientations towards the cortical surface; and third, a flatmap of the cortical volume: a two-dimensional map where each pixel represents a subvolume of voxels along the depth axis, akin to a cortical column. We apply these methods to the somatosensory regions of a digitized version of Paxinos and Watson's rat brain atlas, and define metrics to assess the quality of our results. Among the many applications of the resulting flatmap, we show their usefulness for: decomposing the cortical volume into uniform columnar subvolumes and defining a topographic mapping for long-range connections between subregions. We also generate a flatmap of the isocortex regions of the Allen Mouse Common Coordinate Framework. Combining this with established two-photon tomography data, we then annotate individual barrels and barrel columns in the mouse barrel cortex. Finally, we use the flatmap to visualize volumetric data and long-range axons. We provide an open source implementation of our methods for the benefit of the community.