Image understanding is a difficult problem even in its simplest form because, objects, based on a variety of factors, can have a wide range of intra-class variability. Consider people standing, sitting, facing backward, etc. Beyond recognition, detection requires the localization of the object which can become a costly search problem of the image if not given any heuristics. The problem of detecting objects within in an image has been a vastly investigated problem, but if we consider entities of an even higher, more complex level, such as nuclear plants, how do we begin to approach a solution? In this paper, we explore the state of the art, deformable part models (DPMs), and their applicability for complex object detection in very high-resolution satellite images. A deformable part model, or DPM, is a method used for object detection in images that leverages the fact that objects are inherently made up of a collection of parts. Each part of an object is connected to one or more other parts in a treelike structure. These parts can vary in distance, orientation, or pose with respect to one another but, within some reasonable range, still be considered the skeleton of the same object. DPMs compensate for this property of various objects by utilizing histogram of oriented gradients (HOG) features for object representation at coarse and fine scales, pictorial structures [8], and application of a deformation cost on that pictorial structure. As such, these models can allow for variations in object pose, shape, and viewpoints while still remaining a very specific representation of that object describing not only the object as a whole, but also each of its distinct parts and their spatial relationships. In this paper, we investigate the landscape of research regarding DPMs, how this class of methods for object detection have evolved, and what remains to be explored to make the method more suitable for high-level, complex geospatial object understanding.