Detection of cars in airborne images of typical urban areas has various applications in several domains, such as surveillance, military and remote sensing. It is a tremendously-challenging problem, mainly because of the significant inter-class similarity among various objects in urban environments. In this paper, a novel framework is introduced that adopts a sliding-window approach and it depicts, in a novel way, the local distribution of gradients, colours and texture. A linear support vector machine classifier is used to differentiate between descriptors that belong to cars and descriptors that belong to other objects in a hyperspace of 3838 dimensions. Descriptors are computed over a newly-proposed adaptive distribution of cells that enables the use of various rotation-variant image descriptors. The proposed framework has been evaluated on the Vaihin-gen dataset and results corroborate its superiority as it achieves a higher precision for a given recall than the state of the art.