采用案例归纳推理进行道路网智能选取

自动制图综合集技术、艺术与制图人员经验于一体,长期以来其自动化、智能化研究进展缓慢。基于机器学习的智能化自动综合也成为了制图综合发展过程中必须解决而仍未得到很好解决的核心难题之一。本文提出基于案例归纳学习的道路网智能选取方法。该方法以制图专家道路网选取案例库为学习对象,以决策树算法为推理机,从专家案例库中自动归纳、推理来获取决策树,并转化为满足计算机自动执行的规则集,据此来进行道路网自动选取。从而解决了把难以形式化表达的制图专家经验自动转化为满足计算机自动综合要求的规则,并据此进行智能化自动综合这一难题。最后,采用实例对本方法进行了验证,实验结果表明,该方法能够从专家案例库中自动获取核心规则,并进行自动综合,综合结果能够有效地反映制图专家的制图综合经验,同时具有普适性,从而为智能化自动制图综合发展探索了新的途径。     

模糊因素在道路网选取过程中的应用及评价

道路网选取因素的确定是道路网选取的基础。针对已有算法没有充分考虑模糊因素在道路选取时的作用的问题,提出了道路选取过程中应考虑到的几种模糊因素,借助模糊数学综合评价方法对其进行了综合评价,并将评价结果与基于网眼密度的道路网选取算法进行了融合。实验表明,考虑了模糊因素后的道路网选取在继承了基于网眼密度算法优点的基础上保留了某些交通流量大、具有历史意义或经济价值的"重要"道路,相应地删除了个别同等级但上述意义相对较小的道路,道路网选取结果更加合理,更加符合人们的实际需求。     

模糊因素在道路网选取过程中的应用及评价

道路网选取因素的确定是道路网选取的基础。针对已有算法没有充分考虑模糊因素在道路选取时的作用的问题，提出了道路选取过程中应考虑到的几种模糊因素，借助模糊数学综合评价方法对其进行了综合评价，并将评价结果与基于网眼密度的道路网选取算法进行了融合。实验表明，考虑了模糊因素后的道路网选取在继承了基于网眼密度算法优点的基础上保留了某些交通流量大、具有历史意义或经济价值的“重要”道路，相应地删除了个别同等级但上述意义相对较小的道路，道路网选取结果更加合理，更加符合人们的实际需求。     

GIS环境下制图自动综合

借助GIS的可视化以及GIS的空间分析工具,制图自动综合的研究进入了一个新阶段;一方面,GIS扩大了制图自动综合的内涵,另一方面,制图自动综合推动了GIS的发展;丰富了GIS的研究内容。实际上,制图自动综合应该是GSI系统的重要组成部分或应具有的重要功能。制图自动综合的研究手段和研究方法越来越丰富并取得了很大进展;在总结前人研究的基础上提出了GIS分层支持下的面向地理特征的自动综合,实验表明这种方法是可行的,但仍然存在诸多问题,这种方法的完善必须走基于知识综合的道路。     

基于MongoDB的轨迹大数据时空索引构建方法

近年来,随着计算机技术与无线传感器网络的发展,轨迹大数据越来越得到人们的关注.针对海量轨迹数据在存储与查询中出现的效率问题,文章基于文档型非关系型数据库MongoDB提出了一套基于四叉树的道路网时空索引,实现海量轨迹数据的高效查询.通过对太原市1915辆出租车的50万条轨迹数据进行时空查询,在不同数据量与不同并发数下测试道路网时空索引与MongoDB复合时空索引的效率表现.实验结果显示道路网时空索引在数据量大于10万时有较好表现,并能够适应不同并发数下的时空查询,验证了道路网时空索引构建方法的可行性和高效性.     

移动P2P网络超级节点选取机制研究

针对半分布式结构的移动P2P网络超级节点选取问题,提出一种新颖的超级节点选取机制,全面分析影响超级节点选取的各种因素,按着其特点和类型不同将其分为效益型属性和成本型属性,并在此基础上建立了超级节点选取的带约束多目标优化模型.采用新的约束处理方法,利用免疫克隆算法对超级节点选取问题进行求解.实验结果表明,该机制可以有效降低超级节点的失效率,提高系统查询效率,算法收敛速度快,有较好的可扩展性.     

三维立交结构的欧拉图表达及交互设计方法

针对已有的道路建模技术中建模效果不理想,在大尺度道路网建模中缺乏有效的辅助交互技术支持的问题,通过对立交结构进行分析,提出一种有效的三维立交结构的欧拉图表达及交互设计方法.首先将道路信息预处理,根据处理后的有效数据构建欧拉图,用来表达道路立交结构的拓扑关系;然后利用欧拉图和道路的结构特性计算得到道路的层级关系;再根据控制点、欧拉图的拓扑信息和道路网格,构建立交结构的三维模型;最终构建辅助信息工具,实现对道路网的交互编辑.选取多个类型的立交结构道路网进行实验,结果证明,所提方法在立交结构类型上的应用更广泛,在计算正确率、优化交互编辑过程上有一定优势.     

空间信息检索及其数据库概化技术

在一个空间数据库中,空间数据总是以最细节的信息进行储存,以满足不同应用的需求.制图学中的制图综合就是为不同的应用生成恰当的细节层面.随着空间数据库应用的日益广泛,以及越来越多的基于Internet的空间应用的出现,对空间数据的有效自动综合成为一个紧要问题.深入探讨了当前这一领域存在的问题,提出一种面向数据库的概化处理技术,利用空间索引机制z-value进行扩展,并将其应用到空间数据的自动综合技术中,通过对空间数据库的设计和对空间操作的处理来实现一个有效的空间数据库自动综合系统.     

城市道路网表示的最佳方法选取

城市道路是社会经济活动和人们生活的基础设施,对于城市道路网的表示也是城市快速建模中的重要环节。本文对城市道路网进行分解,归纳出路网中的几类基本的道路类型,根据拓扑学下的图论理论,对归纳出的不同道路类型进行拓扑表示,最后根据时间复杂度和空间复杂度进行不同方法表示的优化对比,得出邻接表可以较高效的进行城市道路网的表示。     

Snake模型初始轮廓选取的研究

针对Snake模型处理复杂背景图像时的初始轮廓自动选取问题,该文采用分水岭算法先对图像进行分割,并将得到的边界作为Snake模型的初始边缘轮廓。由于分水岭算法具有将目标物体从复杂背景中分割开来的优点,使得在应用Snake模型对复杂图像进行分割时减少了人工的干预。经过实验对比,采用分水岭算法对玉米秸秆图像进行边缘轮廓的提取能达到较好的效果,为自动进行Snake模型的计算提供了一种较好的初始轮廓处理方法。     

路网中双色数据集上连续反向k近邻查询处理的研究

近年来,反向最近邻查询(RNN)算法研究得到了普遍的关注,成为了数据库领域的一个研究热点。欧氏空 间中提出了较多的高效算法,而路网中的反向最近邻处理方面所做的工作不够,有关这方面的成果较少。路网中查询 点和数据对象之间以及不同数据对象之间的距离受到路网连通性的影响,欧氏空间中的反向最近部方法在路网中不 适用。反向最近部查询有两种类型:单色反向最近部查询(Monochromatic RNN, MRNN)和双色反向最近部查询(13i- chromatic RNN,13RNN)。到目前为止,仍然没有有效的算法来处理路网中双色数据集上的连续反向k近部查询。因 此,研究路网中双色数据集上连续反向k近部查询是很有意义的。     

基于自适应门控图神经网络的交通流预测

交通流预测是智能交通系统中的重要组成部分,由于交通数据的复杂性,长期而又准确的交通流预测一直是时间序列预测中最具挑战性的任务之一。近年来,研究人员将基于图神经网络的时空图建模方法应用于交通流预测任务,并取得了良好的预测性能。然而,现有的图建模方法仅通过预定义的邻接结构反映道路网络中的空间依赖关系,忽略了各节点之间的序列关联关系对预测的重要性。针对这一局限性,提出了一种自适应门控图神经网络（Ada-GGNN）,其核心为通过空间传递模块同时捕获道路网络的空间结构及自适应的时序相关性,并通过门控机制学习节点上的时间序列特征。在两个真实交通网络数据集PeMSD7和Los-loop上的实验结果证明了该模型具有更优越的性能。     

道路网中基于RRN-Tree的CKNN查询

现有针对基于道路网络的CKNN查询研究,主要是将道路网络以路段和节点的形式进行建模,转化成基于内存的有向/无向图,该模型存在2个问题:一个是道路网络中路段数据量大,导致索引结构分支过多、移动对象更新频繁;另一个是图表示方法不能很好地处理十字路口转向、U型转弯等交通规则。针对此问题,提出道路网中基于RRN-Tree的移动对象CKNN查询算法,包括索引结构设计和移动对象查询算法设计,采用路线对道路网建模,基于网络边扩展方式,实现复杂条件下的道路网络CKNN查询。实验结果表明,在各种网络密度和兴趣点对象分布密度下,与经典的IMA/GMA算法相比,基于RRN-Tree索引方法的查询性能提高1.5倍~2.13倍。     

路网数据流的预测聚集查询新方法研究

时空数据流的聚集查询技术已经成为数据库领域的研究热点。到目前为止,还没有一种有效的全时态聚集索引适用于非欧氏空间的路网数据流聚集查询。实现路网数据流的全时态聚集查询,必须解决:(1)路网的非欧氏空间特性问题;(2)路网上移动对象的重复计数、非均匀分布以及预测聚集问题。Sketch RR-tree解决了非欧氏空间特性和重复计数问题;为解决非均匀分布问题,借鉴草图划分思想,提出动态草图索引结构DynSketch:采用AMH智能划分Sketch RR-tree,使每个划分区域内车辆均匀分布,以提高聚集查询质量;同时,基于DynSketch,结合ES预测模型,提出了路网数据流的预测聚集查询算法。     

Searching continuous nearest neighbors in road networks on the air

Recently, people have begun to deal with location-based queries (LBQs) under broadcast environments. To the best of our knowledge, most of the existing broadcast-based LBQ methods are aimed at Euclidean spaces and cannot be readily extended to road networks. This paper takes the first step toward processing Continuous Nearest Neighbor queries in road Networks under wireless Broadcast environments (CN³B). Our method leverages the key properties of Network Voronoi Diagram (NVD). We first present an efficient method to partition the NVD structure of the underlying road networks into a set of grid cells and number the grid cells obtained, based on which we further propose an NVD-based Distributed air Index (NVD-DI) to support CN³B query processing. Finally, we propose an efficient algorithm on the client side to process CN³B queries. Extensive simulation experiments have been conducted to demonstrate the efficiency of our approach. The results show that our proposed method is about 4 and 7.6 times more efficient than a less-sophisticated D-tree air index based method, in access latency and tuning time, respectively.     

基于数学形态学的道路提取

利用遥感图像进行道路提取已经有了一些研究,如图像分割,基于知识的道路特征识别和数学形态学等,但尚有许多问题需要解决,设计了一种基于数学形态学的遥感图像道路提取算法。该算法首先将遥感图像二值化,然后进行噪声滤除、形态学边缘检测与边缘连接。通过采用ETM+遥感图像进行实验表明,该算法与传统的边缘检测与边缘闭合的算法相比,具有更好的抗噪能力,且精度较高,具有一定的现实意义。     

Continuous range-based skyline queries in road networks

With the ever-growing popularity of smartphone devices in recent years, skyline queries over spatial Web objects in road networks have received increasing attention. In the literature, various techniques have been developed to tackle skyline queries that take both spatial and non-spatial attributes into consideration. However, the existing solutions only focus on solving point-based queries, where the query location is a spatial point. We observe that in many real-life applications, the user location is often represented by a spatial range. Thus, in this paper, we study a new problem of range-based skyline queries (CRSQs) in road networks. Two efficient algorithms named landmark-based (LBA) and index-based (IBA) algorithms are proposed. We also present incremental versions of LBA and IBA to handle continuous range-based skyline queries over moving objects. Extensive experiments using real road network datasets demonstrate the effectiveness and efficiency of our proposed algorithms.     

State of the art on automatic road extraction for GIS update: a novel classification

This paper surveys the state of the art on automatic road extraction for GIS update from aerial and satellite imagery. It presents a bibliography of nearly 250 references related to this topic. The work includes main approaches on general methods of road network extraction and reconstruction, road tracking methods, morphological analysis, dynamic programming and snakes, methods multi-scale and multi-resolution, stereoscopic and multi-temporal analysis, hyper-spectral experiments, and other techniques for road extraction. Likewise, other approaches related in any way with the road extraction topic are also considered. Between them different papers on segmentation, vectorization, optimization, evaluation, semantic nets and neural networks, fusion techniques, fuzzy logic, and other methods are discussed. A novel classification of road extraction methods according to our criteria is included in order to provide a significant contribution to research in this topic.     

Rethinking labelling in road segmentation

ABSTRACT

Deep models are extremely data hungry. Their success is being driven by the availability of large amounts of data for training. For semantic segmentation tasks on aerial and satellite imagery, a major dilemma at present is that it still relies heavily on manual labelling of data. Among these tasks, the semantic segmentation of road is special since it is possible to use auxiliary data, such as GPS track data, to automatically label data. For a better understanding of this possibility, this paper proposes to rethink some basic issues of labelling approaches for roads.

We experimentally investigated the unavoidable class imbalance problem in road segmentation tasks through simulated and real datasets and quantitatively show that class imbalance has a serious detrimental impact on deep model’s generalization performance. We also observed that the detrimental impact even outweighs the benefits of strictly annotating roads – expanding road labels can give deep networks better segmentation accuracy, even though the segmentation location is no longer the edge of the road. We think this is because the impact of class imbalance is much overwhelming than the sensitivity of DNN on the edges of real roads. This finding is valuable for supporting the use of centreline-based approaches in place of edge-based approaches in some applications for better cost-effective solutions.

We proposed a guided Random Sample Consensus (RANSAC) algorithm to determine the optimal expansion ratio of road label. On these bases, we further proposed a general framework to combine two networks to achieve better performance than the state-of-the-art performance of using alone. We attribute this to the alleviation of the class imbalance problem because simply cascading the two networks does not achieve the purpose of improving accuracy in our experiments. We believe that this work is enlightening for studies of road segmentation.     

Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles

Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.