A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data

Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.     

基于角度纹理特征及剖面匹配的高分辨率遥感影像带状道路半自动提取

提出了一种基于角度纹理特征及剖面匹配相结合的高分辨率遥感影像带状道路半自动提取方法.该方法由用户输入道路起点、初始方向及宽度,使用角度纹理特征模型预测初始的道路中线点,以抛物线方程参数构建道路中线轨迹参数模型.使用计算曲率变化的方法验证道路轨迹点,对验证失败的中线点位使用剖面匹配算法进行重新预测并确定,最终提取出该道路中线轨迹.本文使用Visual C 构建了原型系统,对QuickBird及IKONOS影像中具有一定宽度的带状道路进行了提取试验,并与经典的基于剖面匹配的半自动道路提取算法和基于Snakes的半自动道路提取算法进行了对比试验.经试验验证,本算法取得了较为理想的结果.     

基于空间方向相似性的面状矢量空间数据匹配算法

数据一致性匹配处理技术是空间数据融合中的关键技术。采用方向关系矩阵模型进行空间方向关系描述,讨论了一般情况下的空间方向相似性计算,提出了基于空间方向相似性的矢量数据匹配算法流程。最后,以面状目标为研究对象,以某地局部地区居民地数据为例,对基于空间方向相似性的矢量数据匹配算法进行了试验与分析。     

城市目标图像快速更新方法

城市地理环境数据的快速获取与更新是应急测绘保障中的重要任务。本文探讨了一种在应急测绘保障情况下基于城市目标区域的正射影像快速更新方法。该方法能利用最新遥感影像资料对旧正射影像进行基于目标区的局部自动更新。实验证明了该方法的有效性和实用性。     

基于PCA与不变矩的车标定位与识别

在车牌位置确定的情况下,利用车标边缘特点在车牌上方一定范围内检测车标.提出车标似真度的概念,将检测到的车标图像映入PCA生成的特征车标空间,得到的重构图像与原图像进行车标真实性检测,减少车标的误定位;然后利用不变矩的旋转、尺度及平移均不变的特性,定义不变矩的最小矩距离进行车标识别.通过实测车标图像的定位和识别实验表明,该方法是有效和可行的.     

线状中尺度对流系统自动识别

在使用动态模板法识别中尺度对流系统(Mesoscale Convective System,MCS)的算法中引入风暴单体识别跟踪算法(Strom Cell Identification and Tracking,SCIT),实现对MCS内部对流单体的定位,进一步计算出MCS的长宽比。利用该方法对20个多种类型中尺度天气过程进行处理,在选取不同识别阈值情况下对比评估动态模板法。结果表明:(1)动态模板函数、由SCIT得到的平均距离长宽比Wave和最大距离长宽比Wmax均能表征MCS长宽比,且在不同阈值情况下有较好的相关性;(2)动态模板函数与Wave和Wmax变化不一致的时刻是由于质心代表性不足和风暴单体较少引起的趋势变化波动较大造成;(3)综合分析结果表明动态模板函数法识别结果稳定可靠,与实际情况较为一致。     

Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.     

Fast map matching,an algorithm integrating hidden Markov model with precomputation

Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000–45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.     

PIR传感网数据的几何代数建模与行为分析

现有基于PIR（Passive InfraRed）传感器数据的人体行为研究主要局限于运动时空分布、聚类等,对行为轨迹的重建和语义特征的解析相对较少,亟需发展新的建模与行为分析方法。本文尝试利用室内区域PIR传感器监测数据进行时空轨迹重构及其所揭示的运动语义特征研究。本文引入几何代数理论方法,构建传感器场景网络,实现了几何代数空间下动态网络的表达与路径计算,分析了人体运动特征及语义特征,建立最小语义单元,实现了空间数据到语义特征的转化,并可对个人和群体运动的空间区域特征和拓扑特征分析提供支撑。论文将传统PIR传感器网络分析从以几何、统计等为主的信号提取,转变为基于几何代数系统中不同类型代数结构的生成与转化问题,为诸如PIR传感网数据分析一类的非确定问题的求解提供了新的思路和数学基础,可为物联网GIS的构建提供借鉴。