The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Information extraction and three-dimensional (3D) reconstruction of buildings using the vehicle-borne laser scanning (VLS) system is significant for many applications. Extracting LiDAR points, from VLS, belonging to various types of building in large-scale complex urban environments still retains some problems. In this paper, a new technical framework for automatic and efficient building point extraction is proposed, including three main steps: (1) voxel group-based shape recognition; (2) category-oriented merging; and (3) building point identification by horizontal hollow ratio analysis. This article proposes a concept of “voxel group” based on the voxelization of VLS points: each voxel group is composed of several voxels that belong to one single real-world object. Then the shapes of point clouds in each voxel group are recognized and this shape information is utilized to merge voxel group. This article puts forward a characteristic nature of vehicle-borne LiDAR building points, called “horizontal hollow ratio”, for efficient extraction. Experiments are analyzed from two aspects: (1) building-based evaluation for overall experimental area; and (2) point-based evaluation for individual building using the completeness and correctness. The experimental results indicate that the proposed framework is effective for the extraction of LiDAR points belonging to various types of buildings in large-scale complex urban environments.

Research on the forecasting of marine traffic flows can provide a basis for port planning, planning the water area layout, and ship navigation management and provides a practical background for sustainable development evaluation of shipping. Most of the traditional marine traffic volume forecasting studies focus on the variation of the traffic volume of a single port or section in time dimension and less research on traffic correlation of associated ports in shipping networks. To reveal the spatial-temporal autocorrelation characteristics of the shipping network and to establish a suitable space-time forecasting model for marine traffic volume, this paper uses the AIS data from 2011 to 2016 for the South China Sea to construct a regional shipping network. The adjacent discrimination rule based on network correlation is proposed, and the traffic demand between ports is estimated based on the gravity model. On this basis, STARMA (space-time autoregressive moving average) model was introduced for deducing the interaction between he traffic volumes of adjacent ports in shipping network. The experimental results show that (1) there is a significant positive correlation between time and space in the South China Sea shipping network, and this spatial-temporal correlation has the characteristics of time dynamics and spatial heterogeneity; (2) the forecasting accuracy of the marine traffic volume based on the spatial-temporal model is better than the traditional time-series-based forecasting model, and the spatial-temporal model can better portray the spatial-temporal autocorrelation of maritime traffic.

Studying the geospatial association within the urban agglomeration around the South China Sea can provide a basis for understanding the internal development of the China-Association of Southeast Asian Nations (ASEAN) Free Trade Area (CAFTA) and provide ideas for promoting economic and trade cooperation among cities in the region. The purpose of this paper was to reflect the characteristics of the urban agglomeration association network based on big traffic data. Based on trajectory data mining and complex network analysis methods, the automatic identification system (AIS) data was used to construct the traffic flow association network of the urban agglomeration around the South China Sea and then analysis and evaluation were carried out in three aspects: Spatial distribution characteristics of marine traffic flow, analysis of spatial hierarchy and internal difference analysis of the urban agglomeration. The results show the following: (1) The distribution of marine traffic flow within the urban agglomeration around the South China Sea is characterized by polarization and localization and shows a specific power-law distribution; (2) there is a close relationship within the urban agglomeration and the core urban and the marginal urban agglomerations were apparent; (3) subgroup division of urban agglomeration around the South China Sea shows an evident geographic agglomeration phenomenon and there were significant differences between the level of economic development among subgroups; and (4) relative to static factors such as population size and economic aggregate, dynamic flow of information and capital traffic flow plays a more important role in the spatial correlation between cities. Strengthening the links among the three layers of core-intermediate-edge cities through trade and investment means enhancing cooperation among cities within the urban agglomeration and ultimately promoting sustainable regional development.