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11.
深度卷积神经网络(deep convolutional neural network,DCNN)在高分辨率遥感图像自动道路提取领域被广泛应用,但现有方法难以对预测结果中像素间的上下文关系建模。针对此问题,已有研究利用全连接条件随机场(fully connected conditional random field,FullCRF)结合上下文信息对语义分割结果进行二次优化,但无法有效改善道路结构不连续问题。为改善道路结构的完整性,提出一种结合DCNN的短距条件随机场模型(short range conditional random field,SRCRF),SRCRF利用DCNN强大的特征提取能力并控制FullCRF的推理范围缓解过度平滑现象,解决道路提取结果中的结构不连续、不完整问题。实验结果表明,在Zimbawe-Roads数据集与Cheng-Roads数据集中,SRCRF的
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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。 相似文献
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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。 相似文献
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Martin Sudmanns Dirk Tiede Stefan Lang Andrea Baraldi 《International Journal of Digital Earth》2018,11(1):95-112
ABSTRACTThe 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). 相似文献
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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。 相似文献
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Simon Scheider Mark D. Huisjes 《International journal of geographical information science》2019,33(1):28-54
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. 相似文献
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Geographic information has become central for data scientists of many disciplines to put their analyzes into a spatio-temporal perspective. However, just as the volume and variety of data sources on the Web grow, it becomes increasingly harder for analysts to be familiar with all the available geospatial tools, including toolboxes in Geographic Information Systems (GIS), R packages, and Python modules. Even though the semantics of the questions answered by these tools can be broadly shared, tools and data sources are still divided by syntax and platform-specific technicalities. It would, therefore, be hugely beneficial for information science if analysts could simply ask questions in generic and familiar terms to obtain the tools and data necessary to answer them. In this article, we systematically investigate the analytic questions that lie behind a range of common GIS tools, and we propose a semantic framework to match analytic questions and tools that are capable of answering them. To support the matching process, we define a tractable subset of SPARQL, the query language of the Semantic Web, and we propose and test an algorithm for computing query containment. We illustrate the identification of tools to answer user questions on a set of common user requests. 相似文献