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.

Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperature prediction research, this research proposes a temperature prediction model based on the Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) and studies the influence of temperature time-series characteristics, urban spatial location, and other meteorological factors on temperature change in the study area. In this research, multi-meteorological influencing factors and temperature time-series characteristics are used instead of single time-series temperature as influencing factors to improve the time dimension of the input data through time-sliding windows. Meanwhile, considering the influence of meteorological factors in the surrounding area on the temperature change in the study area, we use GCN to extract the urban geospatial location features. The experimental results demonstrate that our model outperforms other models and has the smallest root mean squared error (RMSE) and mean absolute error (MAE) in the following 14-day and multi-region temperature forecasts. It has higher accuracy in areas with stable temperature fluctuations and small temperature differences than in baseline models.

Indoor space is a core part of supporting indoor applications. Most of the existing indoor space models are expressed from three space scales: building, floor, and room, and the granularity is not fine enough, lacking the expression of each functional subspace inside the room. In this study, we first analyzed the spatio-temporal segmentation characteristics of indoor space, and proposed a multi-level indoor space model framework that takes into account multiple types of segmentation. As well, based on the IFC (Industry Foundation Classes) standard, the extension of the indoor functional subspace was realized. The experimental results showed that the indoor space model proposed in this paper can effectively support the expression of functional subspace under the multi-type segmentation based on indoor elements, especially from the aspects of semantics, geometry, relationship, and attribute. This study enriches the granularity of existing indoor models and provides support for refined indoor navigation and evacuation applications.

Concomitant with the acceleration of urbanization, the indoor scene of buildings becoming increasingly complex in recent years. In order to meet the needs of diverse indoor application analysis, the multi-level of detail (LOD) model of indoor scene has been paid increasing attention. However, there are two kinds of LODs in the fields of geographic information and building information, which cannot be effectively integrated. From the perspective of geographic scenario, this paper proposes a new concept of LOD to realize the construction of indoor scene model under different details. According to the effects on indoor space, the indoor components are divided into three semantic levels: enclosing component, connecting component and decorating component. Finally, several examples of indoor models combining semantic LOD, geometric LOD and process LOD are introduced.

Indoor landmarks play a crucial role in the process of indoor positioning and route planning for pedestrians or unmanned devices. Indoor structural landmarks, a type of indoor landmarks, can provide rich steering and semantic descriptions for indoor navigation services. However, most traditional indoor landmark extraction methods rely on indoor points of interest and indoor vector map data. These methods face the problem of difficult acquisition of indoor data and overlook the exploration of indoor structural landmarks. Therefore, this paper innovatively proposes a method for extracting indoor structural landmarks based on the commonly available indoor fire protection plan images. First, the HSV model is employed to eliminate noise from the original image, and vector data of indoor components is obtained using the constructed Canny operator. Subsequently, the visibility is calculated based on the grids of indoor space segmentation. Finally, the identification and extraction of indoor structural landmarks are achieved through grid visibility classification, directional clustering analysis, and spatial proximity verification. This approach opens up new ideas for indoor landmark extraction methods. The experimental results show that the method proposed in this paper can effectively extract indoor structural landmarks, the extraction accuracy of indoor structural landmarks reaches over 90%, verifying the feasibility of using indoor fire protection plan data for landmark extraction and expanding the data sources for indoor landmark extraction.