Massive spatiotemporal data scheduling in a cloud environment play a significant role in real-time visualization. Existing methods focus on preloading, prefetching, multithread processing and ...multilevel cache collaboration, which waste hardware resources and cannot fully meet the different scheduling requirements of diversified tasks. This paper proposes an optimized spatiotemporal data scheduling method based on maximum flow for multilevel visualization tasks. First, the spatiotemporal data scheduling framework is designed based on the analysis of three levels of visualization tasks. Second, the maximum flow model is introduced to construct the spatiotemporal data scheduling topological network, and the calculation algorithm of the maximum data flow is presented in detail. Third, according to the change in the data access hotspot, the adaptive caching algorithm and maximum flow model parameter switching strategy are devised to achieve task-driven spatiotemporal data optimization scheduling. Compared with two typical methods of first come first serve (FCFS) and priority scheduling algorithm (PSA) by simulating visualization tasks at three levels, the proposed maximum flow scheduling (MFS) method has been proven to be more flexible and efficient in adjusting each spatiotemporal data flow type as needed, and the method realizes spatiotemporal data flow global optimization under limited hardware resources in the cloud environment.
The built environment closely relates to the development of COVID-19 and post-disaster recovery. Nevertheless, few studies examine its impacts on the recovery stage and corresponding urban ...development strategies. This study examines the built environment’s role in Wuhan’s recovery at the city block level through a natural experiment. We first aggregated eight built environmental characteristics (BECs) of 192 city blocks from the perspectives of density, infrastructure supply, and socioeconomic environment; then, the BECs were associated with the recovery rates at the same city blocks, based on the public “COVID-19-free” reports of about 7,100 communities over the recovery stages. The results showed that three BECs, i.e., “number of nearby designated hospitals,” “green ratio,” and “housing price” had significant associations with Wuhan’s recovery when the strict control measures were implemented. At the first time of reporting, more significant associations were also found with “average building age,” “neighborhood facility development level,” and “facility management level.” In contrast, no associations were found for “controlled residential land-use intensity” and “plot ratio” throughout the stages. The findings from Wuhan’s recovery pinpointing evidence with implications in future smart and resilient urban development are as follows: the accessibility of hospitals should be comprehensive in general; and the average housing price of a city block can reflect its post-disaster recoverability compared to that of the other blocks.
Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. ...Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.
High-rise residential building façades (HRBFs), given their size and abundant façade information, pose a challenge for conventional parsing methods. This paper presents FaçadeGraph, an approach for ...parsing the information of HRBFs into hierarchical attributed graphs. The method decomposes HRBF information into five hierarchical layers: ternary, floor, unit, space, and component. The façade elements are identified as semantics information with geometric attributes. The topological relationships between the elements are classified into affiliation, connection, aggregation, and decoration. The efficacy of FaçadeGraph was evaluated through the analysis of 36 HRBFs in China. The result showed that FaçadeGraph is effective in transforming diverse façade designs into consolidated graphs for automated syntax analyses. The paper contributes to the knowledge body of façade design by serving as an analytical tool for design feature analysis and underlying the development of generative HRBF design.
•An attributed graph-based façade parsing approach is proposed.•A hierarchical structure is used to indicate façade elements for semantic analysis.•Universally applicable rules are defined for façade representation.•The framework is validated by a dataset of façades on 36 high-rise buildings.
Urban material stock (UMS) represents elegant thinking by perceiving cities as a repository of construction materials that can be reused in the future, rather than a burdensome generator of ...construction and demolition waste. Many studies have attempted to quantify UMS but they often fall short in accuracy, primarily owing to the lack of proper quantification methods or good data available at a micro level. This research aims to develop a simple but satisfactory model for UMS quantification by focusing on individual buildings. Generally, it is a “bottom‐up” approach that uses building features to proximate the material stocks of individual buildings. The research benefits from a set of valuable, “post‐mortem” ground truth data related to 71 buildings that have been demolished in Hong Kong. By comparing a series of machine learning‐based models, a multiple linear regression model with six building features, namely building type, building year, height, perimeter, total floor area, and total floor number, is found to yield a satisfactory estimate of building material stocks with a mean absolute percentage error of 9.1%, root‐mean‐square error of 474.13, and R‐square of 0.93. The major contribution of this research is to predict a building's material stock based on several easy‐to‐obtain building features. The methodology of machine learning regression is novel. The model provides a useful reference for quantifying UMS in other regions. Future explorations are recommended to calibrate the model when data in these regions is available.
•Nature Exposure Index (NEI) defined on window views and walkability of natural sites.•Holistically assessed physical-visual nature exposures for the built environment.•Pareto optimality-based ...identification of areas with low-level nature exposures.•NEI-enabled analytics for probabilistic outputs and robustness of linear weightings.•A case study of a high-rise, high-density area with 519 buildings for validation.
Urban dwellers enjoy nature exposure in the neighborhood built environment through visual and physical ways, such as window views and outdoor activities. However, existing studies and analytics examine these pathways separately, leading to underinformed urban planning practices such as difficult prioritizing urban areas with both low-level nature exposures. The underinformation problem is particularly severe for high-rise, high-density cities that embrace high-level vertical diversity. This study aims to propose bi-objective analytics of 3D visual-physical nature exposures, for holistic – rather than separated – assessments. First, a floor-level Nature Exposure Index (NEI) is defined with visual and physical components. The visual component NEIv is assessed by window view imagery and deep transfer learning, while the physical component NEIp reflects the mean time from the floor to the nearest natural sites (e.g., nature parks and seaside) through the 3D pedestrian network. Then, bi-objective optimization-based analytics is designed for (i) identifying buildings and blocks with holistically low-level visual-physical nature exposures using NEI and (ii) examining probabilistic outputs and robustness of linear weighting schemes. A case study of 519 buildings showed that the NEI-enabled bi-objective analytics is automatic, effective, and inexpensive. Interviews with field experts confirmed that the analytics provides comprehensive evidence for a holistic identification of high-rise, high-density areas in need of nature exposure for landscape management and urban planning.
Compact building models are demanded by global smart city applications, while high-definition urban 3D data is increasingly accessible by dint of the advanced reality capture technologies. Yet, ...existing building reconstruction methods encounter crucial bottlenecks against high-definition data of large scales and high-level complexity, particularly in high-density urban scenes. This paper proposes a Building Section Skeleton (BSS) to reflect architectural design principles about parallelism and symmetries. A BSS atom describes a pair of intrinsic parallel or symmetric points; a BSS segment clusters dense BSS atoms of a pair of symmetric surfaces; the polyhedra of all BSS segments further echo the architectural forms and reconstructability. To prove the concepts of BSS for automatic compact reconstruction, this paper presents a BSS method for building reconstruction that consists of one stage of BSS segments hypothesizing and another stage of BSS segments merging. Experiments and comparisons with four state-of-the-art methods have been conducted on 15 diverse scenes encompassing more than 60 buildings. Results confirmed that the BSS method achieves frontiers in compactness, robustness, geometric accuracy, and efficiency, simultaneously, especially for high-density urban scenes. On average, the BSS method reconstructed each scene into 623 triangles with a root-mean-square deviation (RMSD) of 0.82 m, completing the process in 110 s. First, the proposed BSS is an expressive 3D feature reflecting architectural designs in high-density cities, and can open new avenues to city modeling and other urban remote sensing and photogrammetry studies. Second, for practitioners in smart city development, the BSS method for building reconstruction offers an accurate and efficient approach to compact building and city modeling. The source code and tested scenes are available at https://github.com/eiiijiiiy/sobss.
Display omitted
•Building Section Skeleton (BSS) is proposed with novel definitions of BSS atoms and segments.•BSS revamps traditional shape skeletons to reflect architectural design principles about parallelism and symmetry.•A BSS method consisting of two stages is developed for compact building reconstruction from urban point clouds.•The BSS method of reconstruction was confirmed compact, robust, geometrically accurate, and efficient.
Large-scale assessment of window views is demanded for precise housing valuation and quantified evidence for improving the built environment, especially in high-rise, high-density cities. However, ...the absence of a semantic segmentation dataset of window views forbids an accurate pixel-level assessment. This paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong. The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain. Experimental results of training a well-known deep learning (DL) model, DeepLab V3+ , on CIM-WV, achieved a high performance (per-class Intersection over Union (IoU) ≥ 86.23%) on segmenting major landscape elements, i.e., building, sky, vegetation, and waterbody, and consistently outperformed the transfer learning on a popular real-world street view dataset, Cityscapes. The DeepLab V3+ model trained on CIM-WV was robust (mIoU ≥ 72.09%) in Hong Kong Island and Kowloon Peninsula, and enhanced the semantic segmentation accuracy of real-world and Google Earth CIM-generated window view images. The contribution of this paper is three-fold. CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics. Secondly, comparative analysis shows a more accurate window view assessment using DL from CIM-WV than deep transfer learning from ground-level views. Last, for urban researchers and practitioners, our publicly accessible DL models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics.
PDF
