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A parallel implementation of nearest neighbor analysis based on GPGPU

A parallel implementation of nearest neighbor analysis based...

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International conference on Geoinformatics

作者： Zhao, Yong Chen, Bin Fang, Yu Huang, Zhou Liu, Yuehu Yu, Hao Institute of Remote Sensing and Geographic Information System Peking University Beijing China

ISBN: (纸本)9781612848488

Nearest neighbor analysis is one of the classic methods to find out the tendency of the observed point dataset. With the explosion of spatial data, conventional implementation of nearest neighbor analysis cannot present high performance towards large amount of dataset. So in this paper, a parallel implementation of nearest neighbor analysis is proposed, with parallelization of computing the nearest neighbor distance of each point. Compared with CPU, now GPU can provide more powerful capacity of processing floating point operations and has more multiprocessors for parallel processing. So we develop the parallel program of nearest neighbor analysis with CUDA (Compute Unified Device Architecture) in terms of GPGPU (General-Purpose computing on Graphics Processing Units). In our experiments, when the number of points is large, the speedup of the parallel implementation can achieve more than 10, compared with the conventional implementation in CPU. © 2011 IEEE.

关键词： Graphics processing unit

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The GEOSS clearinghouse high performance search engine

The GEOSS clearinghouse high performance search engine

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International conference on Geoinformatics

作者： Liu, Kai Yang, Chaowei Li, Wenwen Li, Zhenlong Wu, Huayi Rezgui, Abdelmounaam Xia, Jizhe Center for Intelligent Spatial Computing George Mason University Fairfax VA United States Heilongjiang Bureau of Surveying and Mapping Harbin China Center for Spatial Studies University of California Santa Barbara CA United States State Key Laboratory of Information Engineering in Surveying Wuhan University Wuhan China

ISBN: (纸本)9781612848488

The Global Earth Observation (GEO, 2005) was envisioned as a prelude to a Global Earth Observation System of Systems (GEOSS). The Common Infrastructure (GCI) is a geospatial cyberinfrastructure to facilitate the easy discovery, access, and utilization of Earth observation data, information, tools and services through standardized metadata (ISO 19139). The GEOSS Clearinghouse is the engine that drives the entire GCI. It provides a search capability against existing catalogues from GEO members and participating organizations, highlights the range of functionality possible, and creates a basis for a more persistent operational capability. The Center for Intelligent Spatial computing at George Mason University (CISC) worked with the Federal Geographic Data Committee (FGDC) to research and develop such a clearinghouse and was later selected by GEO as the GEOSS clearinghouse. By Mar.3, 2011, 29 catalogs with 110 K metadata had been registered/harvested into the clearinghouse. A high performance based on Lucene and GeoTools search engine is integrated in the clearinghouse. All the metadata are converted into ISO 19139 and stored in the GEOSS clearinghouse database in the harvest process. Based on ISO 19139 template, text in each field can be easily parsed for text index with Lucene, and also spatial bounding box can be easily gotten for spatial index with GeoTools. With the integration of Lucene and GeoTools, both local and remote users can search against the hundreds of thousands of metadata to receive response in less than 2 second. © 2011 IEEE.

关键词： Search engines

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One efficient compression method used on WebGIS

One efficient compression method used on WebGIS

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International conference on Geoinformatics

作者： Zhao, Yanwei Cheng, Zhenlin Dong, Hui Fang, Jinyun Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781612848488

The features of mass spatial data and the limit of Internet bandwidth are key issues which restrict the WebGIS application performance. How to implement efficient vector data compression is important during the stage of data transmission. To get high compression ratio, we propose one multilevel vector data compression method which combine lossy compression and lossless compression technology. According to the resolution of the map, we ignore some vector coordinates based on visualization. Moreover, we apply coding technique to geographic coordinates to achieve higher compression ratio. After illustrating the design and implementation of this method, we conduct experiments to compare the performance of traditional compression technique and the proposed method. The result shows the significant compression ratio (about 90%) which enhance the data transmission efficiency to a large extent. © 2011 IEEE.

关键词： Data transfer

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EVALUATING MODIS VEGETATION INDICES USING GROUND BASED MEASUREMENTS IN MOUNTAIN SEMI-NATURAL MEADOWS OF NORTHEAST PORTUGAL 30

EVALUATING MODIS VEGETATION INDICES USING GROUND BASED MEASU...

引用

30th IEEE International geoscience and remote sensing Symposium (IGARSS) on remote sensing - Global vision for Local Action

作者： Cunha, Mario Pocas, Isabel Marcal, Andre R. S. Rodrigues, Arlete Pereira, Luis S. Univ Porto Fac Ciencias Oporto Portugal FCUP Ctr Investigacao Ciencias Geoespaciais Porto Portugal Tech Univ Ctr Engn Biossistemas CEER ISA UTL Lisbon Portugal

ISBN: (纸本)9781424495665

The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (vi) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite vi from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of vis and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDvi) and Enhanced Vegetation Index (Evi) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each vi time series, as well as to extract a number of NDvi and Evi metrics by computing derivatives. There was weak to reasonable agreement between vis-metrics from MODIS and ground based derived phenology. The NDvi had a great sensitivity to crop growth changes during start of growth season, whereas the Evi exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both vi from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODISEvi. These results suggest a great sensibility of MODISEvi to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

关键词： Vegetation Agriculture remote sensing

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Research and implementation on spatial data storage and operation based on Hadoop platform

Research and implementation on spatial data storage and oper...

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2010 2nd IITA conference on geoscience and remote sensing, IITA-GRS 2010

作者： Wang, Yonggang Wang, Sheng Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing China Beijing Easymap Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9781424485154

As the deep development of spatial information sharing service, it brings forward high request to usability and expansibility of supporting system. Based on large-scale scalable server cluster, cloud computing brings hopes to resolves the existing difficult problems in the domain of geospatial information service. In this paper, we imported cloud computing technology including MapReduce model and Hadoop platform into the domain of geographic information system (GIS). Those key technology problems in the application of GIS such as spatial data storage, spatial index and spatial operation were described and studied in detail. We evaluated the performance and efficiency of spatial operation in Hadoop experiment environment with the real world data set. It demonstrates the applicability of cloud computing technology in computing-intensive spatial applications. © 2010 IEEE.

关键词： MapReduce

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Parallel K-Dimensional Tree Classification Based on Semi-Matroid Structure for remote sensing Applications

Parallel K-Dimensional Tree Classification Based on Semi-Mat...

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conference on Satellite Data Compression, Communications, and Processing vi

作者： Chang, Yang-Lang Chen, Zhi-Ming Liu, Jin-Nan Chang, Lena Fang, Jyh Perng Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Natl Taiwan Ocean Univ Dept Commun & Guidance Engn Taipei Taiwan

ISBN: (纸本)9780819483065

Satellite remote sensing images can be interpreted to provide important information of large-scale natural resources, such as lands, oceans, mountains, rivers, forests and minerals for Earth observations. Recent advances of remote sensing technologies have improved the availability of satellite imagery in a wide range of applications including high dimensional remote sensing data sets (e. g. high spectral and high spatial resolution images). The information of high dimensional remote sensing images obtained by state-of-the-art sensor technologies can be identified more accurately than images acquired by conventional remote sensing techniques. However, due to its large volume of image data, it requires a huge amount of storages and computing time. In response, the computational complexity of data processing for high dimensional remote sensing data analysis will increase. Consequently, this paper proposes a novel classification algorithm based on semi-matroid structure, known as the parallel k-dimensional tree semi-matroid (PKTSM) classification, which adopts a new hybrid parallel approach to deal with high dimensional data sets. It is implemented by combining the message passing interface (MPI) library, the open multi-processing (OpenMP) application programming interface and the compute unified device architecture (CUDA) of graphics processing units (GPU) in a hybrid mode. The effectiveness of the proposed PKTSM is evaluated by using MODIS/ASTER airborne simulator (MASTER) images and airborne synthetic aperture radar (AIRSAR) images for land cover classification during the Pacrim II campaign. The experimental results demonstrated that the proposed hybrid PKTSM can significantly improve the performance in terms of both computational speed-up and classification accuracy.

关键词： K-dimensional tree Semi-matroid structure high dimensional remote sensing images Graphics processing units (GPU)

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Research and implementation on spatial data storage and operation based on Hadoop platform

Research and implementation on spatial data storage and oper...

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IITA International conference on geoscience and remote sensing (IITA-GRS)

作者： Yonggang Wang Sheng Wang Institute of Remote Sensing Applications Chinese Academy and Sciences Beijing China Beijing Easy map Information Technology Company Limited Beijing China

关键词： Roads Indexes Computational modeling Buildings Spatial databases Encoding Geographic Information Systems

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Evaluating MODIS vegetation indices using ground based measurements in mountain semi-natural meadows of Northeast Portugal

Evaluating MODIS vegetation indices using ground based measu...

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IEEE International Symposium on geoscience and remote sensing (IGARSS)

作者： Mário Cunha Isabel Poças Andre R.S. Marcal Arlete Rodrigues Luís S. Pereira Faculdade de Ciências Universidade do Porto Portugal Centro de Engenharia de Biossistemas CEER ISA/UTL Universidade Técnica de Lisboa Portugal

The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (vi) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite vi from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of vis and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDvi) and Enhanced Vegetation Index (Evi) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each vi time series, as well as to extract a number of NDvi and Evi metrics by computing derivatives. There was weak to reasonable agreement between vis-metrics from MODIS and ground based derived phenology. The NDvi had a great sensitivity to crop growth changes during start of growth season, whereas the Evi exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both vi from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODIS Evi . These results suggest a great sensibility of MODIS Evi to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

关键词： MODIS Vegetation Vegetation mapping remote sensing Pollution measurement Atmospheric measurements

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Research and Implementation on Spatial Data Storage and Operation Based on Hadoop Platform

Research and Implementation on Spatial Data Storage and Oper...

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2010 Second IITA International conference on geoscience and remote sensing (IITA-GRS 2010)

作者： Yonggang Wang Institute of remote sensing Applications Chinese Academy of Sciences Beijing,China Sheng Wang Beijing Easymap Information Technology Co.,Ltd Beijing,China

As the deep development of spatial information sharing service,it brings forward high request to usability and expansibility of supporting system. Based on large-scale scalable server cluster,cloud computing brings hopes to resolves the existing difficult problems in the domain of geospatial information service. In this paper,we imported cloud computing technology including MapReduce model and Hadoop platform into the domain of geographic information system (GIS). Those key technology problems in the application of GIS such as spatial data storage,spatial index and spatial operation were described and studied in detail. We evaluated the performance and efficiency of spatial operation in Hadoop experiment environment with the real world data set. It demonstrates the applicability of cloud computing technology in computing-intensive spatial applications.

关键词： geographic information system Hadoop cloud computing MapReduce geospatial service

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Deployment of real-time satellite remote sensing infrastructure to support disaster mitigation: A NATO Science for Peace collaboration project with Research Universities in Turkey, Egypt and the USA

Deployment of real-time satellite remote sensing infrastruct...

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International conference on Recent Advances in Space Technologies (RAST)

作者： Gulay Altay Okan Ersoy Magdy Abdel Wahab Gamal El Afandi Mohammed Shokr Tarek El Ghazawi Mohamed A. Mohamed Belal Eleithy Islam Abou El-Magd Larry Biehl Darion Grant Gilbert Rochon Souleymane Fall Kandilli Observatory & Earthquake Research Institute Boayaziçi University Turkey School of Electrical & Computer Engineering Purdue University USA Dept. of Meteorology & Astronomy Cairo University Egypt Dept. of Meteorology & Astron. El Azhar University Egypt Environment Canada Toronto Ontario Canada High Performance Computing Lab. George Washington Univ. Washington D.C. Washington DC USA Chief GIS Officer UN Dept. of Peacekeeping Operations (DPKO) Chad National Authority for Remote Sensing & Space Sciences (NARSS) Aswan Egypt National Authority for Remote Sensing & Space Sci.(NARSS) Giza Cairo Egypt Purdue Terrestrial Observatory Purdue University West Lafayette Indiana USA

The authors delineate the specific roles of the research partner institutions from Turkey, Egypt and the USA, in planning and implementing the North Atlantic Treaty Organization (NATO) Science for Peace sponsored Kamal Ewida Earth Observatory (KEEO), a network of real-time satellite remote sensing ground stations, being established over the next three years in Egypt, with a tracking station for polar orbiting satellites at Cairo University, and a networked geostationary receiving station for the European Space Agency's Meteosat being deployed at Al Azhar University. The primary objective of the project is to facilitate early warning and mitigation of a wide range of biogenic and anthropogenic disasters. The project will also address mitigation of epidemics and epizootics, through identification and monitoring of infectious disease vector and reservoir habitat. Some examples of common concern among participating countries are climate change and its impacts, the land use problems in agriculture, air pollution problems in major cities such as Cairo and Istanbul, recent epidemics such as the bird flu, swine flu and oil spills along the seashores. Archival and real-time remote sensing and generation of near-real-time spatial data products, utilizing high performance computing clusters, are planned throughout the life cycle of disaster management, including vulnerability assessment, infrastructure safeguards, early warning, emergency response, humanitarian relief, as well as post-disaster damage assessment, reconstruction and societal recovery.

关键词： remote sensing Collaboration Educational institutions Satellite ground stations Influenza geoscience Observatories Space stations Monitoring Diseases

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