Accurate and rapid mapping of seismic building damage is essential to support rescue forces and estimate economic losses. Traditional methods have limitations: ground-based mapping is slow and largely limited to facade information, and image-based mapping is typically restricted to vertical (roof) views. Here, we assess the value of photogrammetrically processed airborne oblique, multi-perspective Pictometry data, in a two-step approach: (a) supervised classification into facades, intact roofs, destroyed roofs and vegetation using 22 image-derived features, and (b) combining the classification results from different viewing directions into a per-building damage score adapted from the European Macroseismic Scale (EMS 98) for damage classification (no-moderate damage, heavy damage, destruction). Overall classification accuracies for the four classes and for the building damage of 70 percent and 63 percent, respectively, were achieved. Image stereo overlap helped classify facades, but problems with the relatively vague EMS damage class definitions were encountered, and subjectivity in training data generation affected overall classification by up to 10 percent.