Manual inspections of glass façade of high rising buildings are expensive, time-consuming and potentially life-threatening for both inspectors and pedestrians on the street. Advances in machine learning for image/video analysis and availability of affordable unmanned aerial vehicles (UAVs) with onboard video recording and processing sensors provide opportunities for smart, safe and automatic glass façade inspections. This paper is concerned with developing an effective solution for recognizing cracked glass panels, which can be installed on board a UAV. From static 2D photographic images, the proposed solution analyzes textural patterns of smooth glass surface and crack segments, linearity of detected crack segments, geometrical characteristics of crack curvatures and the crack pixel patterns, captures these discriminative features for glass cracks using Uniform Local Binary Pattern (ULBP), histograms of linearity, geometrical curvature descriptors with fixed length connected pixel configurations, and accordingly classifies images of cracked and non-cracked glass panels using a kNN classifier. Experimental results with images of different resolutions acquired by a UAV drone in a real office building setting and images collected through Google search demonstrate that the proposed solution achieves promising results with accuracy rates in excess of 80% and even as high as 91% despite the presence of reflections.