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.

Preserving the architectural heritage of traditional historical districts is a crucial aspect of urban renewal. Traditional design workflows are time-consuming and subjective. Current data-driven design methods generate specific style images but are labor-intensive. Recent research highlights Stable Diffusion models’ potential in generating high-fidelity images based on prompts. However, research applying these models to historical districts is scarce, with challenges in creating effective prompts and training parameters. This study proposes a framework combining Stable Diffusion models with expert system-based techniques to generate architectural facades from professional prompts. We constructed a dataset of traditional arcade facades, trained Low-Rank Adaptation (LoRA) models, and integrated ControlNet models to enhance controllability. Experimental results showed our models excelled in precision, realism, and diversity. Both qualitative and quantitative evaluations, along with practical application tests, confirmed our approach aids designers and prompts engineers, contributing to the preservation of architectural heritage and the renewal of urban historical districts.

The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven method utilizes a wide range of knowledge resources, such as historical documents, architectural drawings, and photographs, commonly used to guide and optimize the conservation, restoration, and management of architectural heritage. Recently, the emergence of artificial intelligence-generated content (AIGC) technologies has provided new solutions for creating architectural facades, introducing a new research paradigm to the renovation plans for historic districts with their variety of options and high efficiency. In this study, we propose a workflow combining Grasshopper with Stable Diffusion: starting with Grasshopper to generate concise line drawings, then using the ControlNet and low-rank adaptation (LoRA) models to produce images of traditional Minnan architectural facades, allowing designers to quickly preview and modify the facade designs during the renovation of traditional architectural clusters. Our research results demonstrate Stable Diffusion’s precise understanding and execution ability concerning architectural facade elements, capable of generating regional traditional architectural facades that meet architects’ requirements for style, size, and form based on existing images and prompt descriptions, revealing the immense potential for application in the renovation of traditional architectural groups and historic districts. It should be noted that the correlation between specific architectural images and proprietary term prompts still requires further addition due to the limitations of the database. Although the model generally performs well when trained on traditional Chinese ancient buildings, the accuracy and clarity of more complex decorative parts still need enhancement, necessitating further exploration of solutions for handling facade details in the future.