As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.

With the popularity of various portable mobile devices with positioning functions, a large amount of spatial‐temporal trajectory data has emerged. To effectively compress large‐scale vehicle trajectory data and serve intelligent transportation system, we propose a spatial‐temporal trajectory data compression algorithm based on vehicle motion pattern recognition. The algorithm recognizes vehicles' turning behaviour and variable speed behaviour during the driving process through the analysis of vehicle motion patterns, and extracts the trajectory's turning feature points and variable speed feature points, so as to achieve online compression of the vehicle trajectory. By means of large‐scale experimental datasets, the authors compare this algorithm with representative trajectory compression algorithms in various performance metrics. The experimental results indicate that as an online vehicle trajectory compression algorithm, the proposed algorithm is superior to representative compression algorithms in compression accuracy and computational efficiency, and the compression results can reflect semantic motion information such as turning patterns and variable speed patterns in the trajectory. Therefore, this algorithm has comprehensive advantages, which is foundational for trajectory data mining in intelligent transportation systems.

The popularization of smart phones and the large-scale application of location-based services (e.g., exercises, traveling and food delivery via cycling) have resulted in the emergence of massive amounts of personalized cycling trajectory data, spurring the demand for map navigation based on cycling trajectories. Therefore, in the current paper, we propose a cycling trajectory-based navigation algorithm without the need for road network data support. The proposed algorithm focuses on extracting navigation information from a given trajectory and then guiding others to the destination along the original trajectory. In particular, the algorithm analyzes the coordinate and azimuth angle data collected by the built-in positioning and direction sensors of mobile smart phones to identify several turning modes from the provider’s cycling trajectory. In addition, the interference of the traffic conditions during data collection is considered in order to improve the recognition accuracy of the turning modes. The turning modes in the trajectory are subsequently transformed into navigation information and shared with users, so as to realize the shared navigation of the cycling trajectory. Experimental results indicate that the algorithm can accurately extract the turning feature points from cycling trajectory data, recognize various turning modes and generate correct navigation messages, thereby guiding users to arrive at the destination safely and accurately along the original trajectory. The algorithm is independent of electronic map platforms and does not require road network data support.

This paper uses a small watershed entity model to simulate the rainfall experiment and combines traditional water and sediment observation and terrain three-dimensional laser scanning technology to reveal the characteristics of erosion and sediment yield in small watersheds and the law of channel sediment transport and quantitatively describe the response mechanism of terrain changes in small watersheds to the layout of silt dams and rainfall intensity. Rainfall intensity with three types (30, 60, and 120 mm/h) under three soil conservation measure types (none dam, single dam, and double dams) was simulated, and a small watershed entity model was adopted. The changes in topography were recorded by a Focus 3D laser scanner (Faro) for each experiment. The main results were as follows: (1) Soil erosion under the effect of rainfall occurs on the slope of the watershed and in the gully, while deposition usually occurs on the gentle slope of the gully or in low-lying areas. (2) When the runoff volume is small, deposition occurs easily in the gully, and vice versa. (3) The increase in the number of silt dams deployed has a small effect on the rate of runoff yield on the small watershed, but the limitation on the rate of sand production is especially obvious. Silt dam measures have a good flood and sand reduction effect on small- and medium-intensity rain, but for high-intensity rain, their runoff and sand reduction effect will be reduced, so rainfall is the dominant factor in the formation of soil erosion. Our results provide the scientific basis for identifying key parts of soil erosion and for the rational arrangement of soil and water conservation measures in loess areas.

Vector road networks are vital components of intelligent transportation systems and electronic navigation maps. There is a pressing need for efficient and rapid dynamic updates for road network data. In this paper, we propose a series of methods designed specifically for geometric change detection and the topological consistency updating of multi-source vector road networks without relying on complicated road network matching. For geometric change detection, we employ buffer analysis to compare various sources of vector road networks, differentiating between newly added, deleted, and unchanged road features. Furthermore, we utilize road shape similarity analysis to detect and recognize partial matching relationships between different road network sources. For incremental updates, we define topology consistency and propose three distinct methods for merging road nodes, aiming to preserve the topological integrity of the road network to the greatest extent possible. To address geometric conflicts and topological inconsistencies, we present a fusion and update method specifically tailored for partially matched road features. In order to verify the proposed methods, a road central line network with a scale of 1:10000 from the official institution is employed to geometrically update the commercial navigation road network of a similar scale in the remote area. The experiment results indicate that our method achieves an impressive 91.7% automation rate in detecting geometric changes for road features. For the remaining 8.3% of road features, our method provides suggestions on potential geometric changes, albeit necessitating manual verification and assessment. In terms of the incremental updating of the road network, approximately 89.2% of the data can be seamlessly updated automatically using our methods, while a minor 10.8% requires manual intervention for road updates. Collectively, our methods expedite the updating cycle of vector road network data and facilitate the seamless sharing and integrated utilization of multi-source road network data.