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1. Large-scale urban building function mapping by integrating multi-source web-based geospatial data

   https://doi.org/10.1080/10095020.2023.2264342  

   Chen, Wei; Zhou, Yuyu; Stokes, Eleanor C.; Zhang, Xuesong (October 2023, Geo-spatial Information Science)

   Morphological (e.g. shape, size, and height) and function (e.g. working, living, and shopping) information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling. Due to the limited availability of socio-economic geospatial data, it is more challenging to map building functions than building morphological information, especially over large areas. In this study, we proposed an integrated framework to map building functions in 50 U.S. cities by integrating multi-source web-based geospatial data. First, a web crawler was developed to extract Points of Interest (POIs) from Tripadvisor.com, and a map crawler was developed to extract POIs and land use parcels from Google Maps. Second, an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints. Third, the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions (i.e. hospital, hotel, school, shop, restaurant, and office). The accuracy assessment indicates that the proposed framework performed well, with an average overall accuracy of 94% and a kappa coefficient of 0.63. With the worldwide coverage of Google Maps and Tripadvisor.com, the proposed framework is transferable to other cities over the world. The data products generated from this study are of great use for quantitative city-scale urban studies, such as building energy use modeling at the single building level over large areas. 

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2. RFCNet: Enhancing urban segmentation using regularization, fusion, and completion

   Zhang, Xiaowei; Aliaga, Daniel (July 2022, Computer vision and image understanding)

   Image segmentation is a fundamental task that has benefited from recent advances in machine learning. One type of segmentation, of particular interest to computer vision, is that of urban segmentation. Although recent solutions have leveraged on deep neural networks, approaches usually do not consider regularities appearing in facade structures (e.g., windows are often in groups of similar alignment, size, or spacing patterns) as well as additional urban structures such as building footprints and roofs. Moreover, both satellite and street-view images are often noisy and occluded, thus getting the complete structure segmentation from a partial observation is difficult. Our key observations are that facades and other urban structures exhibit regular structures, and additional views are often available. In this paper, we present a novel framework (RFCNet) that consists of three modules to achieve multiple goals. Specifically, we propose Regularization to improve the regularities given an initial segmentation, Fusion that fuses multiple views of the segmentation, and Completion that can infer the complete structure if necessary. Experimental results show that our method outperforms previous state-of-the-art methods quantitatively and qualitatively for multiple facade datasets. Furthermore, by applying our framework to other urban structures (e.g., building footprints and roofs), we demonstrate our approach can be generalized to various pattern types. 

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3. Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas

   https://doi.org/10.1007/s44212-024-00046-8  

   Zhu, Chunwu; Ye, Xinyue; Du, Jiaxin; Hu, Zhiheng; Shen, Yang; Retchless, David (June 2024, Urban Informatics)

   Abstract Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures. 

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4. Generative Building Feature Estimation From Satellite Images

   https://doi.org/10.1109/TGRS.2023.3242284  

   He, Liu; Shan, Jie; Aliaga, Daniel (January 2023, IEEE Transactions on Geoscience and Remote Sensing)

   Urban and environmental researchers seek to obtain building features (e.g., building shapes, counts, and areas) at large scales. However, blurriness, occlusions, and noise from prevailing satellite images severely hinder the performance of image segmentation, super-resolution, or deep-learning-based translation networks. In this article, we combine globally available satellite images and spatial geometric feature datasets to create a generative modeling framework that enables obtaining significantly improved accuracy in per-building feature estimation and the generation of visually plausible building footprints. Our approach is a novel design that compensates for the degradation present in satellite images by using a novel deep network setup that includes segmentation, generative modeling, and adversarial learning for instance-level building features. Our method has proven its robustness through large-scale prototypical experiments covering heterogeneous scenarios from dense urban to sparse rural. Results show better quality over advanced segmentation networks for urban and environmental planning, and show promise for future continental-scale urban applications. 

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5. Chapter 13: Building information modeling, natural language processing, and artificial intelligence for automated compliance checking

   https://doi.org/0.4337/9781839105524.00022  

   Zhang, R.; El-Gohary, N. (March 2022, Research Companion to Building Information Modeling)

   Lu, W.; Anumba, C. (Ed.)

   The digital and integrated representation of the physical and functional characteristics of buildings enabled by building information modeling (BIM) provides a computational environment for automated compliance checking (ACC) of building designs. The integration of natural language processing (NLP) and artificial intelligence (AI) with BIM brings further opportunities for ACC – it can empower BIM with text analytics and AI capabilities, thereby injecting intelligence and automation in the compliance checking processes. This chapter highlights emerging approaches that aim to facilitate and harness the marriage of BIM, NLP, and AI to enable the next generation of automated compliance checking systems (ACC) systems. This chapter (1) reviews different types of BIM-based ACC systems that leverage NLP and AI techniques, (2) discusses how NLP and AI techniques are applied in regulatory text analytics tasks and BIM information analytics tasks in the context of ACC, and (3) discusses the future trends of BIM-based ACC systems. 

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