2009 17th International Conference on Geoinformatics最新文献

{"title":"WASP: An innovative sensor observation service with web-/GIS-based architecture","authors":"Jen-Chu Liu, Kuo-Yu Chuang","doi":"10.1109/GEOINFORMATICS.2009.5293493","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293493","url":null,"abstract":"Geographic Information System (GIS) can locate, manages, and presents data or location by coordinate information. GIS-based applications are tools that allow users to create interactive queries, analyze and show the spatial information on maps. On the other hand, Wireless Sensor Network (WSN) is widely used. There are huge amount of data retrieved by sensors sent to the server for query by users. The raw data are usually hard to be understood by users. If the raw data can be transferred into add-on valued data and shown on the web pages, the user can obtain more useful and realizable information. This paper proposed an innovated sensor observation service with web-based and GIS-based architecture, which is named WSN Application Service Platform (WASP). WASP is a data management center, which designed with the concept of Services Oriented Application (SOA) as a Cloud Service. WASP is the combination of Internet/Web-based services and components, database systems, users, and WSN communities. The main goal of WASP is providing an easy and straightforward mechanism of data presentation for users. Users can query and obtain the valuable information and realize the meanings of these data through webbased interfaces, which is defined in Sensor Web Enablement (SWE) by Open Geospatial Consortium (OGC). In WASP, all sensors provide their location information to data center and form a community. WASP provides remote control functions for WSN to increase the data retrieval efficiency based on OGC SWE Sensor Observation Service (SOS), Sensor Planning Service (SPS), and Sensor Alert Service (SAS). We demonstrated two applications using WASP in Taiwan. The WASP architecture is a helpful and efficient tool for user to construct a sensor observation service.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132145101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting spatio-temporal outliers with kernels and statistical testing","authors":"James P. Rogers, Daniel Barbará, C. Domeniconi","doi":"10.1109/GEOINFORMATICS.2009.5293481","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293481","url":null,"abstract":"Outlier detection is the discovery of points that are exceptional when compared with a set of observations that are considered normal. Such points are important since they often lead to the discovery of exceptional events. In spatio-temporal data, observations are vectors of feature values, tagged with a geographical location and a timestamp. A spatio-temporal outlier is an observation whose attribute values are significantly different from those of other spatially and temporally referenced objects in a spatio-temporal neighborhood. It represents an object that is significantly different from its neighbors, even though it may not be significantly different from the entire population. The discovery of outliers in spatio-temporal data is then complicated by the fact that one needs to focus the search on appropriate spatio-temporal neighborhoods of points. The work in this paper leverages an algorithm, StrOUD (Strangeness-based Outlier Detection algorithm), that has been developed and used by the authors to detect outliers in various scenarios (including vector spaces and non-vectorial data). StrOUD uses a measure of strangeness to categorize an observation, and compares the strangeness of a point with the distribution of strangeness of a set of baseline observations (which are assumed to be mostly from normal points). Using statistical testing, StrOUD determines if the point is an outlier or not. The technique described in this paper defines strangeness as the sum of distances to nearest neighbors, where the distance between two observations is computed as a weighted combination of the distance between their vectors of features, their geographical distance, and their temporal distance. Using this multi-modal distance measure (thereby called kernel), our technique is able to diagnose outliers with respect to spatio-temporal neighborhoods. We show how our approach is capable of determining outliers in real-life data, including crime data, and a set of observations collected by buoys in the Gulf of Mexico during the 2005 hurricane season. We show that the use of different weightings on the kernel distances allows the user to adapt the size of spatio-temporal neighborhoods.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133945919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractal study on infrared thermal-imaging","authors":"Wu Ying, Sun Mingqing, Li Zhuoqiu","doi":"10.1109/GEOINFORMATICS.2009.5293024","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293024","url":null,"abstract":"Infrared thermal imaging technology is one new method of the modern non-destructive testing. Expanding the qualitative analysis to quantitative characterization is hot and difficult of the infrared thermal imaging technology, that is to say, expanding assessing whether damage and judging injury location to structural integrity evaluation. The key lies in the extraction and description of the infrared thermal imaging characteristics parameters. This paper did fractal study on the experimental samples of infrared thermal imaging, including infrared thermal imaging graphics processing, fractal dimension calculation, realization of calculation program and gray, network and scale and so on, which is the foundation for further study on concrete structure damage and fracture or fracture criterion and the experimental calibration.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121908780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A system for visualizing spatiotemporal urban datasets","authors":"Stefan Hertel, M. Wagner, R. Westermann","doi":"10.1109/GEOINFORMATICS.2009.5293315","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293315","url":null,"abstract":"In this paper we present a system for the interactive exploration of large-scale spatiotemporal city models. Our system focuses on interactive techniques that allow the user to navigate freely in space and time, and which can also be used to query particular features of the model. For that purpose we introduce a novel data structure based on a binary space partitioning tree. A four dimensional kD-tree is introduced, which adopts some of the methods that have been used so far for the construction of 3D space partitions. In particular, we extend the SAH method towards its use for partitioning spatiotemporal geometry models. Based on the constructed data structure, our system provides methods to interactively render large-scale city models and to answer spatiotemporal queries. For the visualization we employ rasterization hardware as well as hierarchical occlusion queries that have been adapted to four dimensional models. Spatiotemporal queries are handled by defining a 4D shape and intersecting it with the nodes of the tree. By using a tree-structure as proposed, we will show that large time-varying city models can be explored interactively and at high quality.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The research of interoperability in spatial catalogue service between CSW and THREDDS","authors":"Chengfang Hu, L. Di, Wenli Yang","doi":"10.1109/GEOINFORMATICS.2009.5293503","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293503","url":null,"abstract":"The interoperable sharing of GIS information across organization and communities has become important due to the growing demand of GIS information research and application. In the past several years, interoperability gaps have made cross-protocol and cross-community data access a challenge within the Earth science community. One such gap is between two protocol families developed within the geospatial and Earth science communities. Although it is need to use data from both geoscience community and OGC community, there is no interoperability product between OGC CS/W and THREDDS catalog system. The first problem we faced is the information models heterogeneity between these two catalogues system. This paper will focus on the effort of information model transformation. we will focus on ISO profile in CSW and discuss their interoperability with THREDDS catalogue schema. After that, it describes the detail strategy during the process of element mapping, and illustrates the resolution of the semantic structural heterogeneity and syntax heterogeneity, and the paper also give the method to advance the CSW sever flexibility during the connection with many different profile CSW client.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122044869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualize, analyze and mine satellite imagery using a single tool","authors":"R. Ramachandran, S. Graves, T. Berendes, M. Maskey, C. Chidambaram, S. Christopher, P. Hogan, T. Gaskins","doi":"10.1109/GEOINFORMATICS.2009.5292953","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5292953","url":null,"abstract":"There is a dearth of software tools that allow users to easily visualize, analyze and mine satellite imagery. The few tools that are available are expensive commercial packages that provide limited functionality. As part of a NASA funded project, a software tool named GLIDER is currently being developed to fill this void. GLIDER allows users to visualize and analyze satellite data in its native sensor view. Users can enhance the image by applying different image processing algorithms on the data. GLIDER provides the users with a full suite of pattern recognition and data mining algorithms that can be applied to the satellite imagery to extract thematic information. The suite of algorithms includes both supervised and unsupervised classification algorithms. In addition, users can project satellite imagery and analysis/mining results onto a 3D globe for visualization. GLIDER also allows users to add additional layers to the globe along with the projected image. Users can open multiple views within GLIDER to manage, visualize and analyze many data files all at once.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122234280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mining time-dependent attractive areas and movement patterns from taxi trajectory data","authors":"Y. Yue, Yan Zhuang, Qingquan Li, Qingzhou Mao","doi":"10.1109/GEOINFORMATICS.2009.5293469","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293469","url":null,"abstract":"Mining attractive areas that people interested in and their related movement patterns can lead to instructive insight to transport management, urban planning and location-based services (LBS). The number of visiting that it attracts is used in this paper to measure an area's level of attractiveness (LoA). As one of the most widely used mode of transport, taxi can tell a lot of stories about not only road network traffic condition, but also areas people interested in crossing a day and their related travel patterns, such as travel destination and average travel distance. Conventional taxi trajectory analysis, or more generally, probe vehicle and floating car trajectory analysis, more focuses on road network travel time and average speed estimation. This study from another angle, uses taxi trajectory data to discover attractive areas that people often visit, for instance, hot shopping and leisure places or living and working areas based on their LoA which hereby is represented as the frequency and density of passenger pick-up and drop-off points, because each point represents a certain scope where attractiveness generates. To obtain meaningful patterns, clustering approach is used to group spatiotemporally similar pick-up and drop-off points, because people's interests to these areas varies through time of the day, day of the week, even season of the year. Moreover, a time-dependent travel flow interaction matrix is established, which is a variation of O-D (Origin-Destination) matrix used in transport domain, and can be used to better understand movement patterns by quantizing the attractiveness among clusters. Background geographic information is used to facilitate the understanding of the movement. This study represents a novel application of taxi trajectory data, reveals people's travel demand and movement patterns in a more deep sense to serve transport management, urban planning, as well as spatiotemporal-tailored location search and services.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122028380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic farmland extraction from multi-temporal landsat TM data based on artificial neural network","authors":"M. Bai, Huiping Liu, Wenli Huang, Yu Qiao, Xiaodong Mu","doi":"10.1109/GEOINFORMATICS.2009.5293543","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293543","url":null,"abstract":"It is an important method of the land use change dynamic monitoring to withdraw the land utilization information using remote sensing image accurately and quickly. However, most of them seemed to be immature enough. This paper aims to use the prior knowledge which is established from one land cover map and remote sensing imagery to realize the automatic extraction of specific land cove class from other remote sensing imagery. The TM satellite imageries in Changyang District of Beijing are taken as an example, and the automatic extraction procession introduce various key technology including relative radiometric correction, feature selection and ANN. The results show that the classification accuracies between the mentioned approach and conventional statistical method (MLC) for individual remote sensing image are very close.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123962330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The framework and realization of grid node computing pool of China geological spatial information grid","authors":"Zhong Xie, Ming-Qiang Guo, Liang Wu, Ying Huang","doi":"10.1109/GEOINFORMATICS.2009.5293314","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293314","url":null,"abstract":"This paper analyzes the characteristics of geological spatial information sharing at first. In order to break the current “information island” and implement spatial information sharing among virtual organizations, the concept of China geological spatial information grid is proposed. Focusing on the core techniques of grid virtual and collaborative issues, this paper discusses the framework and mechanism of grid node computing pool which makes up the China geological spatial information grid platform. The concepts and the key techniques of resource aggregator, meta-service and distributed intelligent service engine which compose the grid node computing pool are given. After three years research, now, the China geological spatial information grid which connects five nodes is preliminary constructed and has been on work. It has implemented on-line distributed resources acquisition, search, spatial analyze, download, resources assessment and geological disaster dynamic assessment of the multi-scale geological maps, geochemical maps and hydrological maps. The actual operation has achieved good results.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130419265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor Web service integration for pandemic disease spread simulation","authors":"Genong Yu, L. Di, J. Smith, Bei Zhang, Peichuan Li, Hulin Wang, Min Min","doi":"10.1109/GEOINFORMATICS.2009.5293497","DOIUrl":"https://doi.org/10.1109/GEOINFORMATICS.2009.5293497","url":null,"abstract":"Pandemic diseases, such as avian influenza, can be deadly and disastrous although they do not happen frequently. Simulation is called for to control the spread of such diseases in preparing for the outbreaks. The spread of pandemic diseases can be affected by many factors, including virus vectors (such as migratory birds), climatic changes, and environment en route of virus vector migration. Many datasets and observations need to be assimilated into the simulation systems. The data and observations can be grouped into three types, i.e. satellite remotely sensed observations, in-situ data, and simulated model outputs. Satellite observations provide a timely large-scale coverage of environment changes and weather conditions. In situ data gives detailed ecological observations of birds at different stations and sites for simulation model construction and validation. Global climate models provide predictions of climate/weather changes, especially useful for filling the gaps when actual observations are not available. In this study, the Self-adaptive Earth Predictive System (SEPS), a general framework of Sensor Web service coordination and integration, was used as the basic framework to develop the observation and data assimilation services for the simulation of avian influenza. With the framework, these three types of data and observations are served through three different Web services. Remotely sensed observations are served through either Sensor Observation Service or Web Coverage Service. Climate models and simulation models are wrapped as Web Processing Services. In-situ bird observations are extracted online and populated as Web Feature Services. The integration of these services is managed using the central piece of SEPS, Coordinate and Event Notification Service (CENS). Internally, a Business Process Execution Language (BPEL) engine is used to actually execute the service integration.","PeriodicalId":121212,"journal":{"name":"2009 17th International Conference on Geoinformatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}