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Objectives: In the large-scale of virtual reality scene, it is difficult to add all graphics data into the video memory for rendering. Removing the occluded objects in advance by visible query technology can reduce the amount of data loaded on the display end to improve the rendering efficiency. Therefore, the research of visible query method for regional objects has important application value for real-time rendering of large-scale urban scene. Methods: We put forward a distributed visible query method based on Map-Reduce. In the map phase, we apply a hierarchical axis-aligned bounding box as viewpoint space partition. When the number of 3D objects in viewpoint space partition exceeds the threshold, the axis-aligned bounding box continues to be divided into sub- boxes. After the above process, the map tasks produce GeoTuples with the VSPID as key and visible query candidate set as value. In the reduce phase, a viewpoint is created for each leaf axis-aligned bounding box where the binary space partitioning trees are build and the visible set is calculated using real-time occlusion algorithm. Results: The experiment results with a building compound, containing more than 200 000 geometric solids, in Shenzhen, China show that: (1) There is no simple linear relationship between the running time of distributed visible query and the number of viewpoint space partitions. (2) Running time and parallelism are not simply inversely proportional. The computational efficiency of each process first increases and then decreases with the increase of parallelism. About 48 parallelism, the process has the highest efficiency. (3) Whether the distributed approach is better than the traditional approach depends on the number of 3D objects. After the amount of 3D objects reaches about 40 000, the distributed algorithm begins to be better than the traditional algorithm. Conclusions: The computational experiments reveal the proposed algorithms outperform competitors in terms of the processing efficiency and feasibility, which can meet the requirement of visible query in large-scale scenarios. [ABSTRACT FROM AUTHOR]

Objectives: Facing the demand for mobile intelligent services in the era of internet of everything, it is an urgent need to establish an aerial-ground collaborative intelligent service platform. Due to the limitations of core equipments and algorithms, the development of aerial-ground collaborative mobile intelligent service platform is relatively slow. Methods: Therefore, we break through the core technologies of realtime position and attitude measurement of sensors, develop the airborne and vehicle mounted aerial-ground multi-platform collaborative networking remote sensing observation system, and realize the quality improvement and intelligent and fine processing of multi-source remote sensing images, which can effectively support remote sensing mapping services, and improve the level of automatic processing of remote sensing information. Results: Thus, our research solves the problems of “accuracy, speed and flexibility” in the aerial-ground collaborative intelligent service platform, and realizes the global autonomous positioning of “BeiDou navigation satellite system with position and attitude measuring instrument”, rapid target searching under the aerial-ground collaborative networking, and automatic change checking under intelligent processing. Conclusions: The aerial-ground collaborative mobile intelligent service platform has been widely used in the monitoring of geographical conditions, smart city planning and construction, emergency mapping support and other industries. [ABSTRACT FROM AUTHOR]

Objectives: With the rapid development of the reality acquisition technologies, such as laser scanning and structured light scanning, point cloud has become a high-precision three-dimensional holographic representation for the physics world. As the third important data source, point cloud is very suitable for presenting 3D model and geographic and spatial information, and pushes forward an immense influence on smart city, autonomous driving application and augmented reality. However, the massive, unstructured, and uneven density of point cloud data brings challenges to onboard and offboard storage as well as real-time transmission. Hence, efficient compression methods, which balance between bit rate and quality, are mandatory for ensuring the storage and transmission of such data. Methods: Firstly, we describe different families of approaches in details and summarize the basic technologies that are usually used in 3D point cloud compression. Secondly, we provide detailed description of three open source point cloud codec algorithms and their coding performances. Finally, the promising development tendency of the static point cloud compression is summarized. Results: This paper summarizes the state-of-the-art of domestic and foreign static point cloud compression algorithms, the standard specifications released by moving picture experts group and evaluation metrics for point cloud compression. Conclusions: The purpose is to open up a vision for the further research on the static point cloud compression technology to a certain extent, boost the advancement of theoretical research significance and practical application value in this technical area. [ABSTRACT FROM AUTHOR]

Objectives: The k nearest neighbor query method under the road network environment plays an important role in geographic information system, point of interest discovery, smart city, data mining and material distribution. The existing methods mainly deal with the query problem that the query object and data object are single data type. However, in reality, the data object type of the target object set is often not single, and the query problem will become more complex. The existing nearest neighbor query methods in road network cannot directly solve the neighbor query problem in which the query object is a point and the data object includes the data mixed with points and line segments. In order to make up for the shortcomings of the existing methods, the nearest neighbor query algorithm of mixed complex data in the road network is proposed. Methods: The query process is divided into three parts: Preprocessing, data set reduction and data set refining. Firstly, the data set is preprocessed, the line segments are transformed into points. A mapping relationship is formed between the intersection of line segments in the road network and line segments, and the corresponding line segments are found by querying the location of the points. The query points, data points and Hilbert values are stored using arrays. Secondly, the data object set is refined preliminarily by pruning method and shortest path theorem to reduce the number of data objects, and the data set filtering algorithm is given. Finally, the points in the data set are further pruned to calculate the shortest path network distance from the query point to the points in the candidate set. Compared with these distances, the shortest path distance can be determined, and then the nearest neighbor is accurately found. Results: Experimental results show that the proposed algorithm effectively reduces the data set and the amount of calculation in the query process, further refines the nearest neighbor candidate set and reduces the query scope and the query time. There is no need to store the distance information between all boundary nodes in the road network, so a large amount of redundant distance information is eliminated. The data points and road network data that meet the pruning conditions around the query points are pruned. Therefore, the increase of road network scale has little impact on the time cost of the algorithm. Conclusions: The proposed algorithm can directly deal with the nearest neighbor query of mixed data in the road network environment. When the number of data objects and the scale of the road network are large, the algorithm has obvious advantages in central processing unit running time and input/output cost. [ABSTRACT FROM AUTHOR]

Objectives: Nowadays, a vast number of sensors and observation platforms bring a significant challenge for city perception. In the study, we focus on developing of a smart city tempo‑spatial perception system. The concept of smart city awareness base station (SCABS) is proposed in this study, and the SCABS architecture considers the above problems. Methods: Firstly, we systematically reviewed the requirements and the complexity of smart city perception and summarized the development process and network architecture of smart city perception technology systems. On this basis, the challenges of ubiquitous access, trusted perception and intelligent management in future smart city perception are addressed. Then, this paper sorted out the demand for future city perception services and constructed the architecture of SCABS. Finally, we standardized the technical indicators and service capabilities of SCABS. Results: We constructed the prototype of SCABS. According to the qualitative analysis and experiments based on real environment, we found that SCABS with unified access, integrated management, and edge intelligence can meet the access and management ability of multi‑platform and high‑density sensing resources. Conclusions: SCABS would act an essential role in the smart city perception in the future. As a cyber‑physical infrastructure, it expects to cover various scenarios in the city with multiple heterogeneous perception access and hybrid access of multi‑protocols, and to automatically support the multi‑platform coordinated control. The SCABS meets global, accurate, heterogeneous, and multiple sensory perception. The SCABS is expected to be one of the critical infrastructures of the future smart city. [ABSTRACT FROM AUTHOR]

Objectives: Because of the fast growing acquisition of real‑time spatiotemporal data for various applications such as smart city or real‑time air‑quality monitoring, the traditional database technologies can‑ not satisfy the higher standards for large‑scale data indexing, querying, and storing operations. As the viable alternative, NoSQL databases that are scalable and possess fast input/output capabilities offer potential solutions to accommodate the needs. Methods: We propose a Hilbert curve and Cassandra technologies based approach for efficient indexing and storing of large‑scale spatiotemporal datasets aiming to provide an effective framework for processing, querying, and analyzing large amount of data with spatial and temporal features. For example, the dataset of vehicle trajectories contains valuable spatial and temporal features those are being employed in the real world. The collected spatiotemporal datasets are preprocessed in order to fit the proposed structures for different applications. Specifically, two types of query applications commonly used in the real world are the spatiotemporal range query and query upon vehicle IDs respectively. Two corresponding indexing structures are designed and implemented in order to accommodate the requests. S2 Geometry Library open sourced by Google is utilized to divide the earth surface into grids, and data points fall in grids are assigned with the specific IDs as the keys. The keys and columns are so designed by applying the Hilbert curve and Cassandra techniques that the resultant structures will physically store the spatially neighboring data points close to each other, and they are more suitable for large‑scale spatiotemporal data querying and analyzing applications. Results: The datasets acquired from the real applications are used to conduct the computational experiments to validate the efficiency of the proposed approach. The query efficiency and the time consumed to store large amount of spatiotemporal data are investigated and bench‑ marked against some existing database technologies. Conclusions: The computational experiments reveal the superiority of the proposed approach comparing to the existing methodologies, the required time to store (insert) data in the database is reduced by 6 times while the time needed to query data is decreased by at least 10 times. The efficiency of the proposed methodology is validate further by applying it to query the vehicle trajectories gathering the real‑time air quality data. [ABSTRACT FROM AUTHOR]

City is the place aggregated massive human activities. City is the exchange hub of population flow, goods flow, information flow and currency flow, which is highly dynamic and complex. Smart city provides various tools to acquire spatiotemporal big data, such as satellite and drone remote sensing, mobile sensing, social sensing, crowdsourcing sensing, etc., which enable us to sense urban dynamics. This paper introduces the framework of urban dynamic sensing, describes the typical applications of spatial dynamics, human behavior dynamics and space behavior interaction dynamics, and discusses the problems, such as the uncertainty in spatiotemporal big data, the multi ⁃view ensemble learning in urban sensing, the verification of the urban dynamic results and the cascading influence of multi⁃urban factors. Outlooking the future, the study of urban dynamics should combine with real⁃time Internet of things data to sense multi⁃dimensional, multi⁃spatiotemporal resolution urban dynamic to enable refined urban governance and to effectively solve urban problems. [ABSTRACT FROM AUTHOR]

Physical space and social space constitute the dual space of human society. With the rapid de⁃ velopment and popularization of information and communication technology (ICT), information space has grown into the third space, which constitutes the ternary space of human society together with physical space and social space. Smart cities are the intelligent transformation of cities under the condition of ternary space. Its basic engineering logic is to establish the mapping from physical space and social space to informa⁃ tion space, and then feedback physical space and social space through information space to optimize the ur⁃ ban system and solve the urban problems. Geographic information system (GIS) is a technical science that depicts and expresses physical space and social space from the perspective of space. Establishing the intrin⁃ sic relationship of ternary space through GIS will be the logical basis for the construction of smart cities. Firstly, the GIS technology requirements of smart cities is analyzed from four aspects: Digitization, intelli⁃ gence, visualization and openness. Then, a GIS framework for smart cities is proposed, the core of which is shielding complex heterogeneous data downwards and enabling diverse applications upwards. Finally, the key research contents that need to be focused on is discussed, including unified urban data platform con⁃ struction, digital twins and visualization, two⁃way synchronized spatial intelligence, and open platform ser⁃ vices. [ABSTRACT FROM AUTHOR]

The pole-like objects are the impontant infrastructure in cities, and their automatic extraction is important for fast updating of smart city data. The vehicle-borne mobile measurement system can quickly acquire high-precision point cloud data of the objects in both roadsides. An automatic extraction method for pole-like objects from vehicle-borne laser point cloud data based on circular arc characteristic is proposed. Firstly, according to the target distribution morphology in single and adjacent scanning lines, the non-pole column part in original point cloud is filtered by clustering and denoising. Then, the circular arc point set is searched by constraint random sample consensus (RANSAC) operator, and the 3D statistical regularity of the circular arc are used to identify the column part of pole-like object accurately. Finally, according to the spatial morphology of the target point cloud, the condition of region growing is dynamically determined, and the complete point cloud of different pole-like objects is searched. Experimental results show that this method can effectively reduce noise interference of non-pole-like objects nearby in pole-like objects extraction and has better adaptive ability. [ABSTRACT FROM AUTHOR]

Smart cities cannot be separated from spatiotemporal location big data. This paper summarizes the needs of spatiotemporal location big data for public epidemic events at home and abroad, investigates the specific services that current spatiotemporal location big data can provide in public epidemic events, and discusses the current stage of spatiotemporal location big data services that cannot meet the requirements of public epidemic events. This paper also focuses on how to improve the spatiotemporal location big data services in the next stage to better serve the public epidemic prevention and control, and gives advice on spatio-temporal location service construction at ordinary times and data sharing mechanism in “wartime”. This paper aims to take scientific measures to improve the spatiotemporal big data services, strives to establish a technical support system for public epidemic prevention and control at ordinary times, and introduces a “wartime” spatiotemporal location big data sharing mechanism for epidemic prevention and control. [ABSTRACT FROM AUTHOR]