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In this letter, a method for the construction of low-level saliency maps is presented in tandem with their evaluation on a set of aerial images. One of the key inspirations for the current research lies on the observation that, usually, the most significant man-made structures in a wide-field aerial image resemble the low-level features that can be detected with a bottom-up saliency map. Aerial photography comprises, hence, a natural domain of application for a method that computationally models low-level saliency. With the employment of mechanisms analogous to the neural functions that drive human attention, we propose a bioinspired framework based on sparse coding for the extraction of information about saliency. The suggested algorithm is then evaluated on a novel data set that has been constructed with the utilization of aerial images and the corresponding manually designed ground truth binary maps of salient structures. The results demonstrate the efficiency of the proposed scheme to highlight conspicuous locations in aerial images, revealing the perspectives on the employment of low-level saliency maps in aerial imaging systems.