An efficient method for rendering linear symbols on 3D terrain using a shader language

With the extensive application of virtual geographic environments and the rapid development of 3D visualization analysis, the rendering of complex vector lines has attracted significant attention. Although there are many rendering algorithms in 3D geographic information system (GIS), they are not sufficiently flexible to meet the requirements for rendering linear symbols composed of diverse colors and shapes. However, the interactive rendering of a scene and the accuracy of the symbols are important components for large-scale, complex vector lines. In this paper, we propose a graphics processing unit (GPU)-accelerated algorithm for rendering linear symbols on 3D terrain. Symbol rendering is embedded within the terrain-rendering process, and vector lines are encoded in a 3D texture and then transferred to the GPU. A set of visual properties are used to enrich the expression of symbols with the help of geometric operations in the fragment shader. A series of experiments demonstrate that the proposed method can be utilized for drawing various pixel-exact linear symbols and can achieve real-time rendering efficiency.     

A simplified linear feature matching method using decision tree analysis,weighted linear directional mean,and topological relationships

Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching.     

耦合高维排序权和对偶线性规划的综合指数算法

区域可持续与高质量发展需定期监测并科学评估。综合指数评价是应用最为广泛、信息传输效率最高的评估方法。该方法将表征区域发展质量不同侧面的系列指标加权聚合为单个指数,其核心是采用或客观或主观的方式确权。客观确权基于指标的数值统计特性实现,故权重值随指标值而改变,实非“客观”;主观确权能反映决策者智慧,但指标过多时难以实现。学界新提出的基于耦合排序权和熵权法的综合指标法允许决策者确权时仅给出权重排序,但仅支持低维情况（限制为3个指标）。本文在其基础上,借助对偶线性规划推导,使其适用性不再受限于指标数（即实现高维排序权）。同时,对指标的聚合方式进行修改,扩大了算法的适用范围;对熵权法的使用进行修正,增强了算法结果的可解译性。基于推导结果,本文进一步发展出单排序、复排序、全排序3种不同模式下的综合指数计算方法,以满足决策者主观性强、弱、无等不同情况。最后,将算法用于全球可持续发展格局的时空评价分析。本文新发展的算法同时适用于高低不同维度的指数聚合、可兼顾决策者不同层次的主观参与度,具有较强的普适性。