With the accelerating urbanization process, the population increasingly concentrates in urban areas. In view of the huge population in China and a series of problems in the process of rapid urbanization, there are no unified measures for characterizing the population pattern. This study explores the distribution pattern of the Chinese population and proposes a spatial distribution structure of population using GIS (Geographic Information System) analysis. The main findings are as follows: (1) In 2015, the distribution of population density in China presents a pattern of high in the southeast and low in the northwest based on the county-level administrative regions. The population main lives in the southeast of China based on the “Hu Huanyong Line”. (2) There is a great difference of the spatial correlation between land area, population and GDP (Gross Domestic Product) in China. The economic concentration in China is higher than the population concentration. In the areas where population and GDP are aggregated, per capita GDP is higher. (3) Based on the areas with highly aggregated population and GDP, the spatial distribution structure of population of “1 + 4 + 11” for China’s urbanization is put forward, namely, one national-level aggregated area of population and GDP, 4 regional-level aggregated areas of population and GDP, and 11 local regionally aggregated areas of population and GDP. This spatial structure represents an attempt to explore the direction of China’s urbanization, and it can be used to optimize the spatial development pattern and provide scientific guidance for the future urbanization plan.

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.

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.

Nowadays, mobile laser scanning is widely used for understanding urban scenes, especially for extraction and recognition of pole-like street furniture, such as lampposts, traffic lights and traffic signs. However, the start-of-art methods may generate low segmentation accuracy in the overlapping scenes, and the object classification accuracy can be highly influenced by the large discrepancy in instance number of different objects in the same scene. To address these issues, we present a complete paradigm for pole-like street furniture segmentation and classification using mobile LiDAR (light detection and ranging) point cloud. First, we propose a 3D density-based segmentation algorithm which considers two different conditions including isolated furniture and connected furniture in overlapping scenes. After that, a vertical region grow algorithm is employed for component splitting and a new shape distribution estimation method is proposed to obtain more accurate global shape descriptors. For object classification, an integrated shape constraint based on the splitting result of pole-like street furniture (SplitISC) is introduced and integrated into a retrieval procedure. Two test datasets are used to verify the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method can achieve better classification results from both sites than the existing shape distribution method.

An increasing number of applications require the accurate 3D layout reconstruction of indoor environments. Various devices including laser scanners and color and depth (RGB-D) cameras can be used for this purpose and provide abundant and highly precise data sources. However, due to indoor environment complexity, existing noise and occlusions caused by clutter in acquired data, current studies often require the idealization of the architecture space or add an implication hypothesis to input data as priors, which limits the use of these methods for general purposes. In this study, we propose a general 3D layout reconstruction method for indoor environments. The method combines voxel-based room segmentation and space partition to build optimum polygonal models. It releases idealization of the architectural space into a non-Manhattan world and can accommodate various types of input data sources, including both point clouds and meshes. A total of four point cloud datasets, four mesh datasets and two cross-floor datasets were used in experiments. The results exhibit more than 80% completeness and correctness as well as high accuracy.

Evaluation of the railway network distribution and its impacts on social and economic development has great significance for building an efficient and comprehensive railway system. To address the lack of evaluation indicators to assess the railway network distribution pattern at the macro scale, this study selects eight indicators—railway network density, railway network proximity, the shortest travel time, train frequency, population, Gross Domestic Product (GDP), the gross industrial value above designated size, and fixed asset investment—as the basis of an integrated railway network distribution index which is used to characterize China’s railway network distribution using geographical information system (GIS) technology. The research shows that, in 2015, the railway network distribution was low in almost half of China’s counties and that there were obvious differences in distribution between counties in the east and west. In addition, multiple dense areas of railway network distribution were identified. The results suggest that it might be advisable to strengthen the connections between large and small cities in the eastern region and that the major urban agglomerations in the midwest could focus on strengthening the construction of railway facilities to increase the urban vitality of the western region. This study can be used to guide the optimization of railway network structures and provide a macro decision-making reference for the planning and evaluation of major railway projects in China.