Using Structural Regularities for a Procedural Reconstruction of Urban Environments from Satellite Imagery
Urban models are of growing importance today for urban and environmental planning, geographic information systems, urban simulations, and as content for entertainment applications. Various methods have addressed aerial or ground scale image-based and sensor-based reconstruction. However, few, if any, approaches have automatically produced urban models from satellite images due to difficulties of data noise, data sparsity, and data uncertainty. Our key observations are that many structures in urban areas exhibit regular properties, and a second or more satellite views for urban structures are usually available. Hence, we can overcome the aforementioned issues obtained from satellite imagery by synthesizing the underlying structure layout. In addition, recent advances in deep learning allow the development of novel algorithms that was not possible several years ago. We leverage relevant deep learning techniques for classifying/predicting urban structure parameters and modeling urban areas that address the problem of satellite data quality and uncertainty. In this dissertation, we present a machine learning-based procedural generation framework to automatically and quickly reconstruct urban areas by using regularities of urban structures (e.g., cities, buildings, facades, roofs, etc.) from satellite imagery, which can be applied to not only multiple resolutions ranging from low resolution (e.g., 3 meters) to high resolutions (e.g., WV3 0.3 meter) of satellite images but also the different scales (e.g., cities, blocks, parcels, buildings, facades) of urban environments. Our method is fully automatic and generates procedural structures in urban areas given satellite imagery. Experimental results show that our method outperforms previous state-of-the-art methods quantitatively and qualitatively for multiple datasets. Furthermore, by applying our framework to multiple urban structures, we demonstrate our approach can be generalized to various pattern types. We also have preliminary results applying this for flooding, archaeological sites, and more.