基于本体的地理信息服务查询组件设计

介绍了一种基于本体的服务发现组件设计方法,将基于本体的元数据和基于本体的查询结合起来,提高了基于OGC标准的网络地理服务发现的查询效率和准确性.     

基于语义的地理信息分类体系对比分析

地理信息分类体系之间的语义不一致性,被认为是影响地理信息系统实现在语义层次上信息共享的最大障碍.首先,本文在阐述中国地理信息分类体系之间参照模式的基础上,提出了一种基于语义的地理信息分类体系对比分析方法.其次,对中国现行的几种地理信息标准分类体系进行了详细的对比分析.最后,为中国地理信息分类体系的编制和修订提出了一些建议.     

顾及形态特征和语义信息的双线道路识别方法

双线道路识别与提取是城市路网综合的关键。提出了一种城市双线道路提取的方法。在构建好的道路网眼的基础上,综合考虑双线道路网眼形态特征以及构成网眼路段之间的语义相似度,设计了识别双线道路网眼的综合指标。通过判断网眼综合指标确定最终的双线道路网眼,最后根据识别出的双线道路网眼提取出双线道路。实验表明,该方法能有效提取出城市道路数据中的双线道路。     

运用社交网络分析淹水空间信息之关联性

随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。     

Semantic and syntactic interoperability in online processing of big Earth observation data

ABSTRACT

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).     

基于随机补片和DeepLabV3+的建筑物遥感图像变化检测

为有效解决传统遥感图像变化检测预处理复杂的问题,提出一种基于随机补片和DeepLabV3+的建筑物遥感图像变化检测方法。以ResNet50特征提取网络为基础,创建DeepLabV3+语义分割网络,并在图像和标签中创建大小为224像素×224像素的随机补片作为网络输入,训练建筑物提取网络;修改建筑物提取网络输入层为6通道,通过矩阵运算将两期遥感图像转换为一幅6通道非RGB图像,利用转换后的非RGB图像进行网络训练并验证变化检测精度。实验1利用ENVI5.3软件,采用马氏距离法进行变化检测;实验2采用改进的U-Net网络和随机补片,完成网络训练和精度验证;实验3使用实验2的训练数据和验证数据,采用随机补片和DeepLabV3+网络进行变化检测网络训练及精度验证。实验结果表明,该方法实验1、实验2、实验3建筑物变化检测平均交并比分别为24.43%、83.14%、89.90%,边界轮廓匹配分数分别为61.47%,80.24%、96.51%。     

一种针对规则冲突的规则集语义比较算法

应用DFD(Diverse Firewall Design)方法进行防火墙规则集设计时,需使用规则集语义比较算法,判断多个规则集是否语义一致。规则冲突是导致规则集语义不一致的主要原因。因此,需要研究针对规则冲突的规则集语义比较算法。而现有算法时空复杂度较高,性能低下。文中提出了1种基于关键数据包选取映射的规则集语义比较算法(KPCM)。KPCM算法针对规则冲突选取关键数据包,并通过比较这些数据包在不同规则集中的处理动作,判断这些规则集是否语义一致。理论分析和测试表明,KPCM算法时空性能均优于现有算法。     

基于FME语义映射的数据转换及应用

在详细介绍FME语义映射和转换规则的基础上,分析了FME的空间数据转换机制和简单语义转换规则的不足,并基于自定义映射文件提出了一种在DOS环境下进行批量数据转换的高效方法,最后以DGN到DWG格式的数据转换实例探讨了实施FME语义转换的具体步骤。     

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.