Indoor emergency response plays a critically important role in disaster management for cities, which must consider the evacuation of people in a dynamic indoor environment. The spatial model is the foundation for the specific analysis of indoor emergency responses, such as evacuations. The current spatial model for evacuation has three primary pitfalls: (1) it primarily focuses on static spatial information, such as rooms, doors, and windows, and lacks dynamic information, such as events and sensors; (2) it mainly focuses on the horizontal space and the static scene and lacks a multi-story component that considers the different properties of stairs compared to planar areas; and (3) it places emphasis on the indoor navigation calculation with a 2D/3D network, which lacks individual properties that can support more complicated analysis, such as congestion and stagnation. In this paper, we propose a dynamic indoor field model with three typical characteristics. (1) It includes not only static information but also dynamic information, such as outdoor and indoor building geometry, sensors, fire spread, and personnel behavior. (2) It supports multi-story buildings from the macro level (building level and floor level) to the micro level (room level and individual level) based on horizontal and vertical indoor space. (3) It supports spatial calculations based on a three-dimensional space grid and can analyze potential congestion and stagnation during evacuation. We design a corresponding evacuation method that supports individual evacuation route finding and evacuation assessment. We perform a series of analyses of the applicability of the proposed model and the efficiency of the designed evacuation method based on multi-story evacuation studies. The simulation includes a total evacuation population exceeding 7000 individuals, and the analysis suggests that the new model and algorithm are effective in planning indoor emergency routes that avoid potential congestion or stagnation.

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.

With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach.