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Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial-intelligence-guided tasks, such as land cover mapping and ground-object mapping, most deep-learning-based architectures fail to extract scale-invariant features, resulting in poor performance accuracy. In this context, the article proposes a superpixel-aided multiscale convolutional neural network (CNN) architecture to avoid misclassification in complex urban aerial images. The proposed framework is a two-tier deep-learning-based segmentation architecture. In the first stage, a superpixel-based simple linear iterative cluster algorithm produces superpixel images with crucial contextual information. The second stage comprises a multiscale CNN architecture that uses these information-rich superpixel images to extract scale-invariant features for predicting the object class of each pixel. Two UAV-image-based aerial image datasets: 1) <italic>NITRDrone</italic> dataset and 2) <italic>urban drone dataset</italic> (UDD), are considered to perform the experiment. The proposed model outperforms the considered state-of-the-art methods with an intersection of union of 76.39&#x0025; and 86.85&#x0025; on UDD and NITRDrone datasets, respectively. Experimentally obtained results prove that the proposed architecture performs superior by achieving better performance accuracy in complex and challenging scenarios.