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Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification. [ABSTRACT FROM AUTHOR]

The growing population and accelerating urbanization have caused much illegal occupation of farmland, which seriously threat to national food security, social stability and economic development of our country. Farmland information extraction has become a hot issue in agricultural research field in the world. In addition, farmland mapping is closely related to the food security and is one of the most concerned issues of government departments. However, traditional technology of surveying and mapping is time consuming and labor costing, which is unable to adapt to the precise and effective information acquisition of farmland. The high resolution remote sensing imagery can provide more details of ground truth than low resolution imagery. However, the information mining in high resolution remote sensing imagery faces a big challenge caused by the complex ground environment. Farmland blocks in high resolution remote sensing imagery have various shapes, complicated texture and heterogeneous spectrum. The shape information is one of the most important content of farmland mapping. In this study, high resolution remote sensing imagery from QuickBird was used to precisely extract farmland in hilly area. And the method of farmland extraction combining multi-scale segmentation and optimal scale selection was put forward. Firstly, gradient image is generated by using Sobel gradient operator. In order to enhance the edge information and reduce the irrelevant information for farmland extraction, the multi-scale gradient images are filtered with anisotropic diffusion operator. Secondly, effective scale range of multi-scale gradient images is determined through the entropy difference analysis, which can reduce the amount of calculation of the multi-scale analysis in next stage. Thirdly, a marker driven watershed transform based on minima extension and minima imposition is applied to segment the multi-scale gradient images to produce multi-scale shape information of farmland with precious boundaries. Finally, the optimal scale identification for multi-scale segmentation is obtained by unsupervised segmentation evaluation method based on the global Moran'I and variance to automatically get the farmland block without manual intervention. The experimental results show that the multi-scale segmentation and optimal scale identification approach can be used to accurately discriminate farmland in hilly area. Farmland extraction accuracy of the proposed method is 73.06% which is 22.40% higher than the Mean-shift segmentation method with a better performance in farmland extraction in hilly area. The results can basically meet the requirements of drawing and revision for the large scale thematic mapping of farmland, showing that the proposed method can provide a technical assistance for the surveying and mapping of farmland. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSWith the development of geographic information services, applications based on map data are increasing. Internet and mobile communication technologies have brought great convenience to the distribution, sharing, and acquisition of map data. Unfortunately, it also brings great challenges to protect the security and privacy of confidential map data. Although several encryption algorithms have been developed for map data, most of the existing methods are simple extensions of classical encryption algorithms for text data in cryptography, ignoring special structures and characteristics of geospatial data. Existing methods mainly take the entire map or layers as encryption units, which makes the encryption efficiency for large-scale map data low and limits the ability to perform local and incremental encryption for local areas, making it still challenging to meet the needs of map data in applications, such as autonomous driving. To solve the limitations, a local encryption approach for large-scale vector map data is proposed in this paper considering the structure characteristics of map data. Experiments on map data of different types (including points, lines, and polygons) demonstrate the effectiveness of the proposed method. With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.A local encryption approach for vector map data is proposed, which can be applied to large-scale vector map data and support localized encryption/decryption for user-interested areas instead of the whole map data.The proposed method estimates different encryption parameters for different units which could significantly enhance the resistance to attacks and naturally supports parallel computing.With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.The method has great value and potential for ensuring map data security in ubiquitous GIS-based applications, such as map data sharing and dynamic updating in automatic driving and crowdsourced mapping. [ABSTRACT FROM AUTHOR]