Abstract
Information extracted from aerial photographs has been used for many practical applications such as urban planning, forest management, disaster relief, and climate modeling. In many cases, labeling of information in the photo is still performed by human experts, making the process slow, costly, and error-prone. This article shows how a convolutional neural network can be used to determine the location of ground control points (GCPs) in aerial photos, which significantly reduces the amount of human labor in identifying GCP locations. Two convolutional neural network (CNN) methods, sliding-window CNN with superpixel-level majority voting, and superpixel-based CNN are evaluated and analyzed. The results of the classification and segmentation show that both of these methods can quickly extract the locations of objects from aerial photographs, but only superpixel-based CNN can unambiguously locate the GCPs.