Remote sensing (RS) oblique imagery provides valuable information of buildings’ facades. Facade detection using spectral-, spatial-, and texture-only features does not accurately separate facade and nonfacade regions in a single-view oblique image. Therefore, a facade index and a unimodal thresholding method were proposed to characterize and detect facade regions. This new index, named the probability-spatial facade index (PSFI), first highlighted facade areas. Then, the facade map was created through a histogram-based thresholding. In unimodal histograms, the thresholding procedure was according to the roots of the second derivative of a fourth-degree polynomial model that is fitted to the PSFI’s histogram. All the steps of the proposed method were implemented in the Google Earth Engine cloud computing platform and could automatically handle very high resolution oblique imagery (in terms of both angle and direction) without any limitations. Furthermore, various high- and low-rise buildings could be effectively processed without any assumptions about the structure of facades. The evaluation showed the high performance of the proposed method in different test areas, in which the average overall accuracies in distinguishing facade and nonfacade regions and in separating facade and rooftop regions were 97% and 86%, respectively.