The integration of augmented reality and drones allows past and future landscapes to be visualized from an aerial perspective. However, these visualizations still suffer from the occlusion problem, where the three-dimensional (3D) virtual model displayed in the real world is in front of a real-world object. Currently, city digital twins are essential for the sustainable development of cities and the development of detailed 3D models of cities. By visualizing the city digital twin, augmented reality can facilitate the participation of nonexpert citizens in the decision-making process of urban design, but research examples are limited. Here, using detailed city 3D models, we develop a digital-twin approach to outdoor augmented reality with occlusion handling for both first-person and bird’s-eye views. In a verification experiment, the occlusion handling accuracy of the prototype system was measured to be about 0.8 using intersection over union. The frame rate of the entire prototype system was about 30 fps, and the delay between the controller and the augmented reality device was about 3 s. The internet-based system architecture was developed to integrate augmented reality and drone systems. Our system allows multiple stakeholders involved in building construction projects to observe aerial perspectives of those projects, both on-site and off-site via an internet browser, using augmented reality with occlusion handling.

Precise measuring of urban façade color is necessary for urban color planning. The existing manual methods of measuring building façade color are limited by time and labor costs and hardly carried out on a city scale. These methods also make it challenging to identify the role of the building function in controlling and guiding urban color planning. This paper explores a city-scale approach to façade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. Firstly, we used semantic segmentation to extract building façades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based façade color measurement method and a multi-label deep learning-based building classification method. Next, the field survey data were used as the ground truth to verify the accuracy of the façade color measurement and building function classification. Finally, we applied our approach to generate façade color distribution maps with the building classification for three metropolises in China, and the results proved the transferability and effectiveness of the scheme. The proposed approach can provide city managers with an overall perception of urban façade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

Automatic object removal with obstructed façades completion in the urban environment is essential for many applications such as scene restoration, environmental impact assessment, and urban mapping. However, the previous object removal typically requires a user to manually create a mask around unwanted objects and obtain background façade information in advance, which would be labor-intensive when implementing multitasking projects. Moreover, accurately detecting objects to be removed in the cityscape and inpainting the static obstructed building façade to obtain plausible images are the main challenges for this objective. To overcome these difficulties, this study addresses the object removal with the façade inpainting problem from the following two aspects. First, we proposed an image-based cityscape elimination method for automatic object removal and façade inpainting by applying semantic segmentation to detect several classes, including pedestrians, riders, vegetation, and cars, as well as using generative adversarial networks (GANs) for filling detected regions by background textures and patching information from street-level imagery. Second, we proposed a workflow to filter unoccluded building façades from street view images automatically and tailored a dataset for the GAN-based image inpainting model with original and mask images. Furthermore, several full-reference image quality assessment (IQA) metrics are introduced to evaluate the generated image quality. Validation results demonstrated the feasibility and effectiveness of our proposed method, and the synthetic image is visually realistic and semantically consistent.

In advanced society, the existing building stock has a high demand for stock renovation, which gives existing buildings new lives, rather than building new ones. During the renovation process, it is necessary to simultaneously achieve architectural, facilities, structural, and environmental design in order to accomplish a healthy, comfortable, and energy-saving indoor environment, prevent delays in problem-solving, and achieve a timely feedback process. This study tackled the development of an integrated system for stock renovation by considering computational fluid dynamics (CFD) and mixed reality (MR) in order to allow the simultaneous design of a building plan and thermal environment. The CFD analysis enables simulation of the indoor thermal environment, including the entire thermal change process. The MR system, which can be operated by voice command and operated on head-mounted display (HMD), enables intuitive visualization of the thermal change process and, in a very efficient manner, shows how different renovation projects perform for various stakeholders. A prototype system is developed with Unity3D engine and HoloLens HMD. In the integrated system, a new CFD visualization method generating 3D CFD animation sequence for the MR system is proposed that allows stakeholders to consider the entirety of changes in the thermal environment.