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1.
Chuanrong Zhang Tian Zhao E. Lynn Usery Dalia Varanka Weidong Li 《Transactions in GIS》2020,24(2):508-525
Disaster response operations require fast and coordinated actions based on real‐time disaster situation information. Although crowdsourced geospatial data applications have been demonstrated to be valuable tools for gathering real‐time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of semantic compatibility and interoperability. To help overcome the semantic incompatibility and heterogeneity problems, we use Geospatial Semantic Web (GSW) technologies. We then combine GSW technologies with Web Feature Service requests to access multiple servers. However, a GSW‐based geographic information system often has poor performance due to the complex geometric computations required. The objective of this research is to explore how to use optimization techniques to improve the performance of an interoperable geographic situation‐awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client‐side optimization techniques for improving the performance of an IGSAS prototype for flood disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on‐the‐fly spatial indexing, tile‐based rendering, efficient algorithms for spatial join, and caching, especially for those spatial‐join geospatial queries that involve a large number of spatial features and heavy geometric computation. 相似文献
2.
Social media networks allow users to post what they are involved in with location information in a real‐time manner. It is therefore possible to collect large amounts of information related to local events from existing social networks. Mining this abundant information can feed users and organizations with situational awareness to make responsive plans for ongoing events. Despite the fact that a number of studies have been conducted to detect local events using social media data, the event content is not efficiently summarized and/or the correlation between abnormal neighboring regions is not investigated. This article presents a spatial‐temporal‐semantic approach to local event detection using geo‐social media data. Geographical regularities are first measured to extract spatio‐temporal outliers, of which the corresponding tweet content is automatically summarized using the topic modeling method. The correlation between outliers is subsequently examined by investigating their spatial adjacency and semantic similarity. A case study on the 2014 Toronto International Film Festival (TIFF) is conducted using Twitter data to evaluate our approach. This reveals that up to 87% of the events detected are correctly identified compared with the official TIFF schedule. This work is beneficial for authorities to keep track of urban dynamics and helps build smart cities by providing new ways of detecting what is happening in them. 相似文献
3.
Extreme localised rainfall events are part of monsoon system occurring every year. Satellite data (TRMM and other geostationary
satellites) together with ground observations will be useful in providing spatial and temporal variability of atmospheric
changes. The short and long term variability is required for better understanding of the local and regional climatic conditions
through detailed modelling. Such information will play a key role in real time data analysis and dissemination system to the
disaster management groups in the country to minimize losses due to these extreme rainfall events. 相似文献
4.
Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria. 相似文献
5.
《International Journal of Digital Earth》2013,6(9):727-749
Disaster management and in particular disaster response phase are highly time-sensitive and dynamic processes, demanding that real-time information reaches disaster responders prior making critical decisions. During the last decade, disaster management has been widely enabled through utilizing spatial data sourcing and related technologies in the whole process of collection, access, and usage of disaster information. Currently, there are unique challenges that cannot be met without incorporating in situ sensing as an emerging technology for sourcing and managing disaster information. These include (1) high temporal and spatial resolution of information, (2) broad range of disaster data, and (3) automated operations. Incorporating in situ sensing into the disaster management process can potentially address such challenges by providing data that support all of these requirements. Following an examination of current concepts and methods for integrating multisourced sensors, a framework of the requirements for integrating in situ sensors for disaster management, is suggested. Based on this framework and its components, an evaluation of the methods is developed and applied. The results highlight that information integration of multisourced sensors is a major challenge and has not yet adequately addressed for sensor data enablement of disaster management. 相似文献
6.
基于空间语义角色的自然语言空间概念提取 总被引：4，自引：1，他引：4
根据空间信息的特点,从定义的空间语义角色入手,通过语义角色标注、短语识别以及概念模式匹配等手段,具体分析了自然语言中的空间实体、实体间空间关系以及空间过程的表达与提取方法。 相似文献
7.
基于短信的时空地震灾情收集与管理 总被引：2，自引：0，他引：2
地震现场各种信自获取与传输是应急指挥工作中的重要内容,有关灾情的资料是政府部门防震减灾决策的重要依据.本文研究了震后灾情信息的分类与编码,探讨了灾情信息的时空特性,阐述了T-GIS在地震灾情信息获取与发布工作中的应用,在基态修正时空数据模型的基础上提出了基于短信的时空地震灾情收集与管理模型,采用ArcGIS二次开发技术... 相似文献
8.
Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach. 相似文献
9.
地震是一种会造成人类生命财产重大损失的突发性自然灾害,震后第一时间启动应急响应并开展灾情的快速评估能有效地减轻地震灾害带来的破坏。空间对地观测技术为宏观性的地震应急与调查工作提供了便捷、经济的途径,随着空间对地观测技术与数据处理技术的不断发展,各国学者对遥感应急调查开展了大量深入的研究工作,相关研究成果已广泛地应用于地震应急的实际工作中。但是,遥感数据类型与处理技术的多样化也带来了应急信息的散乱,导致遥感快速应急响应系统性不强,使得应急服务不持续,一定程度上限制了遥感技术的效能;为此,针对现阶段遥感技术在地震应急调查中的应用情况,在总结地震应急调查常用遥感技术手段的基础上,分析了遥感快速应急响应面临的技术挑战,重点梳理了地震应急不同阶段对遥感数据及应急专题产品产出类型与时效性的现实需求,结合震后灾区影像数据的情况,系统地分析了光学、雷达、激光雷达遥感技术在地震应急调查应用中的技术现状与存在的问题。在实际地震应用案例分析的基础,总结剖析遥感应急工作存在的问题,并重点从海量数据快速处理、震害信息智能化提取、多源数据协同分析3个技术层面论述了遥感地震应急面临的核心困难,基于此,结合在轨数据实... 相似文献
10.
农晓珍 《测绘与空间地理信息》2015,(7)
时空信息是所有内涵数据都具备的基本特性,通过时空信息处理技术建构时空坐标体系,让所有数字典藏的内容得以整合,通过网络开放式交换机制,让原本储存在不同机构的信息,既可以分散储存,又可以彼此引用套合,促成信息统整。本文探讨了利用互联网地理信息系统建构数字典藏计划的时空内涵基础架构,其最主要目的在于扩充原有地理信息系统内涵,发展成时间及空间信息处理技术,建构起时空内涵基础架构,以精确的时空参考系统为基础,建立所有典藏内涵的时间与地理空间标记(geographic footprint),做为信息检索和信息整合的主要依据,并以空间可视化(Spatial Visualization)方式提供更生动、易读的、精准的事件原貌。 相似文献
11.
A Rule-Based Strategy for the Semantic Annotation of Geodata 总被引：2，自引：0，他引：2
Eva Klien 《Transactions in GIS》2007,11(3):437-452
The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule‐based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views. 相似文献
12.
Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. In all phases of disaster risk management including forecasting, emergency response and post-disaster reconstruction, the need for interconnected multidisciplinary open data for collaborative reporting as well as study and analysis are apparent, in order to determine disaster impact data in timely and reportable manner. The extraordinary progress in computing and information technology in the past decade, such as broad local and wide-area network connectivity (e.g. Internet), high-performance computing, service and cloud computing, big data methods and mobile devices, provides the technical foundation for connecting open data to support disaster risk research. A new generation of disaster data infrastructure based on interconnected open data is evolving rapidly. There are two levels in the conceptual model of Linked Open Data for Global Disaster Risk Research (LODGD) Working Group of the Committee on Data for Science and Technology (CODATA), which is the Committee on Data of the International Council for Science (ICSU): data characterization and data connection. In data characterization, the knowledge about disaster taxonomy and data dependency on disaster events requires specific scientific study as it aims to understand and present the correlation between specific disaster events and scientific data through the integration of literature analysis and semantic knowledge discovery. Data connection concepts deal with technical methods to connect distributed data resources identified by data characterization of disaster type. In the science community, interconnected open data for disaster risk impact assessment are beginning to influence how disaster data are shared, and this will need to extend data coverage and provide better ways of utilizing data across domains where innovation and integration are now necessarily needed. 相似文献
13.
中国是农业灾害发生频繁且灾情严重的国家之一。农业保险已成为农民灾后恢复生产和灾区重建的重要资金来源,风险保障和经济补偿作用日益凸显。但是,农业保险面临着信息不对称和经营成本高等问题。而利用3S等空间信息技术,可将保险标的空间化,建立承保标的空间数据库,为承保和理赔工作提供空间数据和分析管理支持,实现＂按图承保＂和＂按图理赔＂,以空间信息技术支持的农业保险创新应用促进了农业保险模式的转变,从而有效地解决农业保险存在的信息不对称和经营成本高等问题,充分发挥农业保险支农惠农的社会管理职能,提升农业保险的风险管理水平,以更好地服务＂三农＂。 相似文献
14.
基于地理事件时变序列的时空数据模型研究与实现 总被引：14，自引：1，他引：14
首先分析比较了已有的时空数据模型的特点，针对其存在的不足，提出并讨论了一种基于地理事件时变序列的时空数据模型，阐述了该模型的时空语义概念和时空拓扑关系，并针对具体的实际应用，在现有成熟的GIS平台上进行扩展，验证了该数据模型的有效性。 相似文献
15.
Due to increasing global urbanization and climate change, the quantification of “human footprints” has become an urgent goal in the fields of biodiversity conservation and regional environment management. A human footprint is defined as the impact of a particular human activity on the Earth’s surface, which can be represented mainly by impervious surfaces (related to industry and urbanization) and cropland (related to agriculture). Here we present a method called sorted temporal mixture analysis with post-classification (STMAP) for mapping impervious surfaces and cropland simultaneously at the subpixel level to fill the demand for precise human footprint information on a national scale. The STMAP method applies a four-endmember sorted temporal mixture analysis to provide the initial fractions of evergreen forests, deciduous forests, cropland, and impervious surfaces as a first step. Endmembers are selected from the sorted temporal profiles of the MODIS-normalized difference vegetation index (NDVI), as guided by a principal component analysis. The yearly maximum land surface temperatures and averaged stable nighttime light are then statistically analyzed to provide the thresholds for post-classification to further separate cropland from deciduous forest and bare land from impervious surface. As the four outputs of STMAP, the fractions of forest, cropland, impervious surfaces and bare land are derived. We used the reference maps of impervious surfaces and cropland obtained from the Landsat/TM and ALOS precise land-use/land-cover map at the subpixel level to evaluate the performance of the proposed method, respectively. Historical satellite images with high spatial resolution were used to further evaluate the cropland results derived with the STMAP method. The results showed that the STMAP method has promising accuracy for estimating impervious surfaces and cropland in Japan. The root mean square errors obtained with the STMAP method were 6.3% for the estimation of impervious surfaces and 9.8% for the estimation of cropland. Our findings can extend the applications of remote sensing technologies in ecological research and environment management on a large scale. 相似文献
16.
17.
Accurate and interpretable prediction of crowd flow would benefit business management and public security. The existing studies are challenged to adapt to the indoor environment due to its complex and dynamic spatial interaction patterns. In this study, we propose a crowd flow predicting method for indoor shopping malls, which simultaneously features temporal variables and semantic factors to suit the shopping mall environment. A deep learning model named DeepIndoorCrowd is presented. The model aims at capturing temporal dependencies and the semantic pattern in crowd flow to generate an accurate multi-horizon prediction. With a multi-term temporal dependency capturing structure, the model is effective in learning both daily and weekly patterns of the indoor crowd flow in a shopping mall and is able to provide the temporal interpretation of the prediction result. Moreover, a semantic-temporal fusion module is introduced to utilize the semantic information of stores in prediction, which has proved to be effective in enhancing the model's ability to learn temporal patterns. Experiments were conducted on a real-world dataset to verify the proposed approach. The ablation study demonstrates that the DeepIndoorCrowd can effectively improve the efficiency and accuracy of the prediction up to 18.7%. In addition, some interesting indoor crowd flow patterns were discovered by analyzing the model's interpretation of the prediction result. The proposed prediction method provides an intuitive way of modeling indoor crowd flow, and the experiment's outcome can help indoor managers better understand stores' flow traffic. 相似文献
18.
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
19.
Geospatial Ontology Development and Semantic Analytics 总被引：3，自引：0，他引：3
I Budak Arpinar Amit Sheth Cartic Ramakrishnan E Lynn Usery Molly Azami Mei-Po Kwan 《Transactions in GIS》2006,10(4):551-575
Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP. 相似文献