Integrating semantic edges and segmentation information for building extraction from aerial images using UNet

Abstract

Understanding urban dynamics, such as estimating population, urban development, and several other uses, necessitates up-to-date large-scale building maps. Since aerial imagery provides enough textural and structural details, it has been utilized as a critical data source for building detection. However, accurate mapping of building objects from aerial imagery is a challenging task. This problem is attributed due to presence of vegetation and shadows in images that present similar spectral values and transparency as a building class. To deal with the issues mentioned above, we offer a new deep-learning structure named MultiRes-UNet network, which is an improved version of the original UNet network. In the proposed network, we utilized the MultiRes block to assimilate the features learned from the data at various scales and comprise some more spatial details. Also, we suggest the incorporation of several convolutional operations along with the skip connections to mitigate the differences between the encode–decoder features. Furthermore, we integrated semantic edge information with semantic polygons to solve the issue of irregular semantic polygons and enhance the boundary of semantic polygons. We tested our network on aerial images for roof segmentation dataset, and the experimental results exhibited that the proposed network can improve the quantitative results of Intersection Over Union to 0.78% after adding semantic edges. We also used state-of-the-art comparative models such as UNet, DeeplabV3, ResNet, and FractalNet networks to show the competency of the introduced network, and the results prove the success of the introduced network for building object extraction from aerial imagery.