As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.

Landslides can result in extensive casualties and huge economic losses. The accurate discrimination of the main slip direction and deformation trajectory is an important prerequisite for studying landslide formation mechanisms and designing landslide control schemes. In the process of landslide evolution over time, the main slip direction also changes dynamically and provides a comprehensive reflection of the landslide displacement state. However, few studies on this topic have been published. In this paper, a new methodology for analyzing slope stability is proposed based on three techniques: interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV), and ground-based interferometric synthetic aperture radar (GB-InSAR). The Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique is combined with an overall analysis of the study area to identify the regions of interest (ROIs) with large deformation and the starting target points, and the fusion results of radar deformation data (RDD) and digital surface model (DSM) data are used to fit the deformation surface field of the ROIs. The gradient descent approach is executed to obtain the running trajectory points of the target masses so that the main slip direction and displacement trajectory in the study area can be predicted at small scales. The measured data for the Hongshiyan landslide in Yunnan Province are used to verify the effectiveness of the method, and the predicted results are consistent with the actual landslide direction. The experimental results show that the method can exactly identify the deformation area, especially in the case of a fast-changing deformation trend. This approach can provide more accurate monitoring area results to support the rapid control and prevention of landslide hazards by analyzing the minimum pixel grid (i.e. points), as the smallest spatial unit at a time interval of minutes. The study shows that the method can efficiently combine space–Earth multisource monitoring data to clarify the main slide direction and improve the postslide trajectory prediction of the slope, which is beneficial for assessing disaster risks and improving landslide prevention and control effects, reflecting the engineering application value of the approach.

The Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) Loosely Coupled (LC) integration framework has been widely adopted due to its simple structure, but it relies on complete GNSS position and velocity solutions, and the rapid accumulation of IMU errors can easily lead to navigation failure when fewer than four satellites are visible. In this paper, GNSS Doppler observations are fused with IMU attitude information within an LC framework. An inter-satellite differential Doppler model is introduced, and the velocity obtained from the differential Doppler solution is transformed into the navigation frame using the IMU-derived attitude, enabling three-dimensional velocity estimation in the navigation frame even when only two satellites are available. Analysis of real vehicle data collected by the GREAT team at Wuhan University shows that the Signal-to-Noise Ratio (SNR) and the geometric relationship between the Satellite Difference Vector (SDV) and the Receiver Motion Direction (RMD) are the dominant factors affecting velocity accuracy. A multi-factor threshold screening strategy further indicates that when $\mathrm{S}\mathrm{NR}>$ 40 and $\mid \mathrm{SDV}·\mathrm{RMD}\mid >0.2$, the Root Mean Square (RMS) of the velocity error is approximately 0.3 m/s and the data retention rate exceeds 44%, achieving a good balance between accuracy and availability. The results indicate that, while maintaining a simple system structure, the proposed Doppler–IMU fusion method can significantly enhance velocity robustness and positioning continuity within an LC architecture under weak GNSS conditions (when more than two satellites are visible but standalone GNSS positioning is still unavailable), and is suitable for constructing low-cost, highly reliable integrated navigation systems.

This paper proposes an effective method for measuring the non-smoothness of time series data: Dirichlet mean energy function. The method expresses the time series data as an n-dimensional vector based on its own properties, and then abstracts the time series model as a chain graph model based on directed graph theory. The incidence matrix of the time series data is established based on the constructed chain graph model, and the Dirichlet mean energy function is defined in the form of matrix function. The Dirichlet mean energy function can quantitatively express the non-smoothness of time series data. The contribution of this paper is to proposes an effective mathematical tool for measuring the non-smoothness of time series data based on graph theory and matrix theory. In future work, we will further validate the validity of this tool in more application areas and extend this method to high-dimensional time series data.