A NOVEL BUILDING RECONSTRUCTION FRAMEWORK USING SINGLE-VIEW REMOTE SENSING IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORKS

Building model reconstruction is one of the remaining challenges for satellite imagery. In this article, we present a framework that leverages single-view remote sensing images to reconstruct buildings in 3D space. The framework consists of two main steps: the powerful feature learning ability in convolutional neural networks is utilized to reconstruct roof-based 3D voxel grid buildings. Another is improving the resolution and accuracy of voxel models through interpolation optimization methods with prior information. By evaluating the experimental results, our method can accurately recover the polygonal buildings structure with several different roof types. The model optimization results have significantly improved in subjective visual effects and objective evaluation.