Unsupervised building footprint extraction using Alpha-Tree Differential Attribute Profiles. The method makes use of the latest, state of the art hierarchical image representation data-structure, the Alpha-Tree. A tree polychotomy scheme know as “attribute zoning” is computed from which Differential Attribute Profile (DAP) vector fields can be accessed. The Alpha-Trees are computed from vector data (multi-spectral imagery) thus the entries of the new DAPs contain dissimilarity values that optimize material separation and lead to accurate and automatic segmentation based on size, shape and radiometry. The method employs additional layers such as the unsupervised LULC and unsupervised built-up extent, for false positive reduction and self-supervised learning. The method concludes in < 3min for typical WorldView-2 multi-spectral data-sets of approximate coverage of 120km x 20km @ 2m spatial resolution. This does not include target contour refinement and vectorization. The image shows a blend in of the original rgb layer and the segmentation result. Object intensity (gray-scale) indicates confidence [0%-100%] that the segmented object is a building. The scene shows the south sector of the city of Kano, Nigeria.