Search

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately. [ABSTRACT FROM AUTHOR]

High-precision road detection from very high resolution (VHR) remote sensing images has broad application value. However, the most advanced deep learning based methods often fail to identify roads when there is a distribution discrepancy between the training samples and test samples, due to their limited generalization ability. In this paper, to address this problem, an open-source data-driven domain-specific representation (OSM-DOER) framework is proposed for cross-domain road detection. On the one hand, as the spatial structure information of the source and target domains is similar, but the texture information is different, the domain-specific representation (DOER) framework is proposed, which not only aligns the distributions of the spatial structure information, but also learns the domain-specific texture information. Furthermore, in order to enhance the representation of the target domain data distribution, open-source and freely available OpenStreetMap (OSM) road centerline data are utilized to generate target domain samples, which are then used in the network training as the supervised information for the target domain. Finally, to verify the superiority of the proposed OSM-DOER framework, we conducted extensive experiments with the public SpaceNet and DeepGlobe road datasets, and large-scale road datasets from Birmingham in the UK and Shanghai in China. The experimental results demonstrate that the proposed OSM-DOER framework shows obvious advantages over the mainstream road detection methods, and the use of OSM road centerline data has great potential for the road detection task. [ABSTRACT FROM AUTHOR]

Global navigation satellite system multipath channel modeling is important for signal simulation and error mitigation in urban scenarios. However, there have not been sufficient studies on channel statistical models for dynamic land vehicles in eastern Asian cities. This paper presents both MULTIPATH and non-line-of-sight (NLOS) channel models for GPS L1CA and BDS B1I signals. They are based on an extensive field dataset collected in the Shanghai-Lujiazui area. In addition, a method is developed to estimate multipath components and NLOS signal parameters in a dynamic channel environment. It is found that the Gamma function best fits the distribution of MULTIPATH channel delays while the exponential function is better suited for the distribution of NLOS channel delays. Both MULTIPATH and NLOS channel signals follow a linear decline average power-delay trend and a zero-mean Gaussian distribution power scattering model. The Doppler fading frequency distribution for MULTIPATH and NLOS channels can be modeled by the Gaussian function except for the MULTIPATH channel signal at the low velocity range (0∼3 km/h), where an absolute exponential distribution is a better fit. The lifetime distribution for MULTIPATH and NLOS channels is represented by the generalized Pareto distribution function. These findings offer insights into the GNSS signal models in urban propagation channels and are essential to the development of accurate urban navigation systems. [ABSTRACT FROM AUTHOR]