Building profile extraction from aerial imagery constitutes a key element in numerous geospatial applications. Rooftop detection has been addressed through a variety of approaches that are, however, rarely capable of coping with conditions such as arbitrary illumination, variant reflections, and complex building profiles. This paper proposes a new method for extracting 2-D rooftop footprints from nadir aerial imagery through a fully automatic approach that handles arbitrary illumination, variant reflections, and complex building profiles without shape priors. The proposed method combines the strength of energy-based approaches with distinctiveness of corners. Corners are assessed using multiple color and color-invariance spaces. A rooftop outline is generated from selected corner candidates and further refined to fit the best possible boundaries through level-set curve evolution that is enhanced via a mean squared error map. Experimental results confirm the ability of the presented system to effectively extract rooftop profiles with an overall average shape accuracy of 84%, correctness of 94%, completeness of 92 %, and quality of 88%.