To support Earth system modeling, we propose a discrete global grid system that expresses multi-resolution spatial data. Specifically, a unified coding model that expresses a grid of nodes, edges, and cells is constructed for a triangular discrete global grid system. To fulfill the requirements of practical applications, we design a code-based topological query method for this grid system and an algorithm to transform between grid codes and geographic coordinates. We evaluate the Global Finite Volume Community Ocean Model (Global-FVCOM) on the triangular discrete global grid system in the proposed uniform coding model. The ocean tidal waves simulated by the Global-FVCOM running on the coded grid are then compared with results obtained using a traditional irregular spherical grid system, and the results display comparable accuracy. The uniform coding model proposed in this paper provides a triangular discrete global grid system that can represent multi-resolution spatial data and can be used in Earth system models. This unified coding model can also be applied to the geographic coordinate system made up of latitudes and longitudes, as well as diamond and hexagonal grids.

An indoor navigation network, simplified from complex building structures, is of primary importance for indoor navigation. Methods for generating such networks are becoming increasingly important as buildings are becoming larger and more complex. However, studies based on the geometry and topology of indoor spaces do not sufficiently account for the key and decision nodes of human wayfinding. This is mainly because the visible perception of pedestrians regarding the corridor path structure is not taken into consideration. In this study, we first propose the indoor visibility map (IVM), calculate the visibility of corridor space, and analyze the relationship between different types of corridors and quantitative visibility parameters of IVM. Based on the visibility analysis, corresponding network construction strategies are suggested for different types of indoor corridors that contain nodes and edges. The experimental results show that the proposed network identifies key locations in the corridor structure that have a decisive role in navigation. Furthermore, the network is simplified to some extent.

Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

Computer Aided Design (CAD) systems play an important role in the production of building designs that are used to assist building constructions. Thus, CAD building drawings contain a large amount of building data important for indoor navigation and other related analysis which are often done in BIM (Building Information Modeling). Therefore, the conversion of CAD-based building data to BIM is necessary to make the data in CAD useful for these applications. However, when mapping to the BIM model, there are semantic ambiguities which will have an important impact on the subsequent analysis of the information converted from CAD. This paper addresses these ambiguity problems by first examining the building information represented in CAD drawings and the mapping relationships between the building information in CAD drawings and that in BIM. Once these relationships are established, the semantic ambiguities existed in the mapping were identified and then corresponding constraints were developed to address these ambiguities. The experiment results show that the approach described in this paper can effectively eliminate the semantic ambiguity of the mapping from CAD to BIM and the converted BIM model can effectively support subsequent analysis.

Global ocean surface currents estimated from satellite derived data based on a regular global grid are affected by the grid’s shape and placement. Due to different neighbourhood relationships, the rectangular lat/lon grids lose accuracy when interpolating and fitting elevation data. Hexagonal grids have shown to be advantageous due to their isotropic, uniform neighbourhood. Considering these merits, this paper aims to estimate global ocean surface current using a global isotropic hexagonal grid from satellite remote sensing data. First, gridded satellite altimeter data and wind data with different resolutions are interpolated into the centre of the global isotropic hexagonal grid. Then, geostrophic and Ekman currents components are estimated according to the Lagerlof Ocean currents theory. Finally, the inversion results are verified. By analyzing the results, we conclude that the ocean surface currents estimated based on the global isotropic hexagonal grid have considerable accuracy, with improvement over rectangular lat/lon grids.

In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes.