Change detection techniques have been widely used in satellite based environmental monitoring. Multi-date classification is an important change detection technique in remote sensing. In this study, we propose a hybrid algorithm called HC-DT/SVM, that tightly couples a Decision Tree (DT) algorithm and a Support Vector Machine (SVM) algorithm for land cover change detections. We aim at improving the interpretability of the classification results and classification accuracies simultaneously. The hybrid algorithm first constructs a DT classifier using all the training samples and then sends the samples under the ill-classified decision tree branches to a SVM classifier for further training. The ill-classified decision tree branches are linked to the SVM classifier and testing samples are classified jointly by the linked DT and SVM classifiers. Experiments using a dataset that consists of two Landsat TM scenes of southern China region show that the hybrid algorithm can significantly improve the classification accuracies of the classic DT classifier and improve its interpretability at the same time.
{"title":"HC-DT/SVM: a tightly coupled hybrid decision tree and support vector machines algorithm with application to land cover change detections","authors":"Jianting Zhang","doi":"10.1145/1869890.1869892","DOIUrl":"https://doi.org/10.1145/1869890.1869892","url":null,"abstract":"Change detection techniques have been widely used in satellite based environmental monitoring. Multi-date classification is an important change detection technique in remote sensing. In this study, we propose a hybrid algorithm called HC-DT/SVM, that tightly couples a Decision Tree (DT) algorithm and a Support Vector Machine (SVM) algorithm for land cover change detections. We aim at improving the interpretability of the classification results and classification accuracies simultaneously. The hybrid algorithm first constructs a DT classifier using all the training samples and then sends the samples under the ill-classified decision tree branches to a SVM classifier for further training. The ill-classified decision tree branches are linked to the SVM classifier and testing samples are classified jointly by the linked DT and SVM classifiers. Experiments using a dataset that consists of two Landsat TM scenes of southern China region show that the hybrid algorithm can significantly improve the classification accuracies of the classic DT classifier and improve its interpretability at the same time.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448964","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}
An estimate of the error between the mean concentration of a released pollutant simulated by an atmospheric dispersion model and the values measured at the ground is obtained using Dynamic Time Warping (DTW). The error measure is relevant to the application with iterative source detection algorithms based on forward numerical transport and dispersion simulations. The new proposed measure is compared with two established error functions commonly used in the literature. A sensitivity study of the error measure to wind direction was performed using real world data from the Prairie Grass field experiment. Whereas both standard measures found smallest error only with a few degrees of wind direction, DTW found the smallest error with a much larger range of wind directions, often as high as 20 degrees.
利用动态时间翘曲(Dynamic Time Warping, DTW)估计了大气扩散模型模拟的污染物平均浓度与地面测量值之间的误差。误差测量与基于正演数值输运和色散模拟的迭代源检测算法的应用有关。并与文献中常用的两种已建立的误差函数进行了比较。利用草原草田间实测数据，进行了误差测量对风向的敏感性研究。虽然两种标准测量方法都只发现了几度风向的最小误差，但DTW在更大的风向范围内发现了最小误差，通常高达20度。
{"title":"Assessment of error in air quality models using dynamic time warping","authors":"Jessica Lin, G. Cervone, P. Franzese","doi":"10.1145/1869890.1869895","DOIUrl":"https://doi.org/10.1145/1869890.1869895","url":null,"abstract":"An estimate of the error between the mean concentration of a released pollutant simulated by an atmospheric dispersion model and the values measured at the ground is obtained using Dynamic Time Warping (DTW). The error measure is relevant to the application with iterative source detection algorithms based on forward numerical transport and dispersion simulations. The new proposed measure is compared with two established error functions commonly used in the literature.\u0000 A sensitivity study of the error measure to wind direction was performed using real world data from the Prairie Grass field experiment. Whereas both standard measures found smallest error only with a few degrees of wind direction, DTW found the smallest error with a much larger range of wind directions, often as high as 20 degrees.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131823158","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}
The need to collect vast amounts of geospatial data is driven by the emergence of geo-enabled Web applications and the suitability of geospatial data in general to organize information. Given that geospatial data collection and aggregation is a resource intensive task typically left to professionals, we, in this work, advocate the use of information extraction (IE) techniques to derive meaningful geospatial data from plain texts. Initially focusing on travel information, the extracted data can be visualized as routes derived from narratives. As a side effect, the processed text is annotated by this route, which can be seen as an improved geocoding effort. Experimentation shows the adequacy and accuracy of the proposed approach by comparing extracted routes to respective map data.
{"title":"Geospatial route extraction from texts","authors":"Efthymios Drymonas, D. Pfoser","doi":"10.1145/1869890.1869894","DOIUrl":"https://doi.org/10.1145/1869890.1869894","url":null,"abstract":"The need to collect vast amounts of geospatial data is driven by the emergence of geo-enabled Web applications and the suitability of geospatial data in general to organize information. Given that geospatial data collection and aggregation is a resource intensive task typically left to professionals, we, in this work, advocate the use of information extraction (IE) techniques to derive meaningful geospatial data from plain texts. Initially focusing on travel information, the extracted data can be visualized as routes derived from narratives. As a side effect, the processed text is annotated by this route, which can be seen as an improved geocoding effort. Experimentation shows the adequacy and accuracy of the proposed approach by comparing extracted routes to respective map data.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432998","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}
Sujing Wang, Chun-Sheng Chen, Vadeerat Rinsurongkawong, F. Akdag, C. Eick
Polygons can serve an important role in the analysis of geo-referenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from different spatial datasets is proposed which consists of a meta clustering module that clusters polygons and a summary generation module that creates a final clustering from a polygonal meta clustering based on user preferences. Moreover, a density-based polygon clustering algorithm is introduced. Our methodology is evaluated in a real-world case study involving ozone pollution in Texas; it was able to reveal interesting relationships between different ozone hotspots and interesting associations between ozone hotspots and other meteorological variables.
{"title":"A polygon-based methodology for mining related spatial datasets","authors":"Sujing Wang, Chun-Sheng Chen, Vadeerat Rinsurongkawong, F. Akdag, C. Eick","doi":"10.1145/1869890.1869891","DOIUrl":"https://doi.org/10.1145/1869890.1869891","url":null,"abstract":"Polygons can serve an important role in the analysis of geo-referenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from different spatial datasets is proposed which consists of a meta clustering module that clusters polygons and a summary generation module that creates a final clustering from a polygonal meta clustering based on user preferences. Moreover, a density-based polygon clustering algorithm is introduced. Our methodology is evaluated in a real-world case study involving ozone pollution in Texas; it was able to reveal interesting relationships between different ozone hotspots and interesting associations between ozone hotspots and other meteorological variables.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123309570","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}
Reconstructing views of real-world from satellite images, surveillance videos, or street view images is now a very popular problem, due to the broad usage of image data in Geographic Information Systems and Intelligent Transportation Systems. In this paper, we propose an approach that tries to replace the differences among images that are likely to be vehicles by the counterparts that are likely to be background. This method integrates the techniques for lane detection, vehicle detection, image subtraction and weighted voting, to regenerate the "vehicle-clean" images. The proposed approach can efficiently reveal the geographic background and preserve the privacy of vehicle owners. Experiments on surveillance images from TrafficLand.com and satellite view images have been conducted to demonstrate the effectiveness of the approach.
{"title":"View reconstruction from images by removing vehicles","authors":"Li Chen, Lu Jin, Jing Dai, J. Xuan","doi":"10.1145/1869890.1869896","DOIUrl":"https://doi.org/10.1145/1869890.1869896","url":null,"abstract":"Reconstructing views of real-world from satellite images, surveillance videos, or street view images is now a very popular problem, due to the broad usage of image data in Geographic Information Systems and Intelligent Transportation Systems. In this paper, we propose an approach that tries to replace the differences among images that are likely to be vehicles by the counterparts that are likely to be background. This method integrates the techniques for lane detection, vehicle detection, image subtraction and weighted voting, to regenerate the \"vehicle-clean\" images. The proposed approach can efficiently reveal the geographic background and preserve the privacy of vehicle owners. Experiments on surveillance images from TrafficLand.com and satellite view images have been conducted to demonstrate the effectiveness of the approach.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127267314","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}
This paper highlights the diversity of spatial data of rural and urban properties, constantly generated by different public institutions, as well as the existing problems of exchange of information among them. Firstly, this work describes the results obtained in the study and development of an agile flexible method to offer support construction, implementation and accompaniment activities of free geo-solutions for the web, aiming at a growing community of users and developers who manipulate geographic data. Next, the development of the OpenICGFw (Integration for Collaborative Geospatial Framework for the Web) that seeks, through a single environment to assist in the integration and collaboration among different sources of spatial data in synchrony with the efforts and specifications of OGC and W3C. To do this, the evaluation study for the construction of the framework is presented where it was possible to apply MCDA-C (Multi Criteria Decision Aiding -- Constructivist) in the identification of the fundamental and elementary aspects for the construction of the framework. Details are presented by means of a case study that illustrates data exported from different geospatial information systems requiring the integration of census, environmental, urban and rural information over the internet. During the discussion the results obtained using this framework are presented, providing, through web mapping applications, the implementation of collaborative strategies seeking the integration of bases distributed for the use of spatial data mining techniques.
{"title":"Framework of integration for collaboration and spatial data mining among heterogeneous sources in the web","authors":"A. Moraes, L. Bastos","doi":"10.1145/1869890.1869893","DOIUrl":"https://doi.org/10.1145/1869890.1869893","url":null,"abstract":"This paper highlights the diversity of spatial data of rural and urban properties, constantly generated by different public institutions, as well as the existing problems of exchange of information among them. Firstly, this work describes the results obtained in the study and development of an agile flexible method to offer support construction, implementation and accompaniment activities of free geo-solutions for the web, aiming at a growing community of users and developers who manipulate geographic data. Next, the development of the OpenICGFw (Integration for Collaborative Geospatial Framework for the Web) that seeks, through a single environment to assist in the integration and collaboration among different sources of spatial data in synchrony with the efforts and specifications of OGC and W3C. To do this, the evaluation study for the construction of the framework is presented where it was possible to apply MCDA-C (Multi Criteria Decision Aiding -- Constructivist) in the identification of the fundamental and elementary aspects for the construction of the framework. Details are presented by means of a case study that illustrates data exported from different geospatial information systems requiring the integration of census, environmental, urban and rural information over the internet. During the discussion the results obtained using this framework are presented, providing, through web mapping applications, the implementation of collaborative strategies seeking the integration of bases distributed for the use of spatial data mining techniques.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131606919","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}
Land degradation, and poverty issues are very common in our world, especially in developing countries in Africa. There are fewer adaptation strategies for climate change in these countries. Ethiopia is a tropical country found in the horn of Africa. The majority of the population live in rural areas and agriculture is the main economic sector. Extensive agriculture has resulted in an unexpected over-exploitation and land degradation. The project locations are Southwestern and Northwestern Ethiopia. The main objectives are to analize the accuracy of land use classification of each sensors, classification algorithms and analyze land use change. Thematic Mapper (TM) and Radar data will be used to classify and monitor land use change. Two consecutive satellite images will be used to see the land use change in the study area (1998, 2008). ERDAS Imagine will be used to resample and spatially register the Radar and TM data. The image classification for this research study is supervised signature extraction. The Maximum likelihood decision rule and C4.5 algorithm will be applied to classify the images. TM and Radar data will be fused by layer staking. The accuracy of the digital classification will be calculated using error matrix. Land change modeler will be used for analyzing and predicting land cover change. The impact of roads, urban and population density on land use change will be analayzed using GIS.
{"title":"Land use analysis using GIS, radar and thematic mapper in Ethiopia: PhD showcase","authors":"Haile K. Tadesse","doi":"10.1145/1869890.1869897","DOIUrl":"https://doi.org/10.1145/1869890.1869897","url":null,"abstract":"Land degradation, and poverty issues are very common in our world, especially in developing countries in Africa. There are fewer adaptation strategies for climate change in these countries. Ethiopia is a tropical country found in the horn of Africa. The majority of the population live in rural areas and agriculture is the main economic sector. Extensive agriculture has resulted in an unexpected over-exploitation and land degradation. The project locations are Southwestern and Northwestern Ethiopia. The main objectives are to analize the accuracy of land use classification of each sensors, classification algorithms and analyze land use change. Thematic Mapper (TM) and Radar data will be used to classify and monitor land use change. Two consecutive satellite images will be used to see the land use change in the study area (1998, 2008). ERDAS Imagine will be used to resample and spatially register the Radar and TM data. The image classification for this research study is supervised signature extraction. The Maximum likelihood decision rule and C4.5 algorithm will be applied to classify the images. TM and Radar data will be fused by layer staking. The accuracy of the digital classification will be calculated using error matrix. Land change modeler will be used for analyzing and predicting land cover change. The impact of roads, urban and population density on land use change will be analayzed using GIS.","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128840343","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":"An Outline of the Global Grid Forum Data Access and Integration Service Specifications","authors":"M. Antonioletti, Amy Krause, N. Paton","doi":"10.1007/11611950_7","DOIUrl":"https://doi.org/10.1007/11611950_7","url":null,"abstract":"","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701598","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":"RRS: Replica Registration Service for Data Grids","authors":"A. Shoshani, A. Sim, Kurt Stockinger","doi":"10.1007/11611950_9","DOIUrl":"https://doi.org/10.1007/11611950_9","url":null,"abstract":"","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127506580","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}
S. Narayanan, T. Kurç, Ümit V. Çatalyürek, J. Saltz
{"title":"Servicing Seismic and Oil Reservoir Simulation Data Through Grid Data Services","authors":"S. Narayanan, T. Kurç, Ümit V. Çatalyürek, J. Saltz","doi":"10.1007/11611950_11","DOIUrl":"https://doi.org/10.1007/11611950_11","url":null,"abstract":"","PeriodicalId":370250,"journal":{"name":"Data Management in Grids","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130566178","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}
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