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A Novel Method for Dynamic Surface Modeling and Real-Time Rendering

A Novel Method for Dynamic Surface Modeling and Real-Time Re...

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21st International conference on Geoinformatics (Geoinformatics)

作者： Yan, Changqing Yue, Tianxiang Liu, Jimin Zhao, Chunyan Shanghai Univ Sci & Technol Dept Informat Engn Tai An Shandong Peoples R China Inst Geograph Sci & Natural Resources Res Beijing Peoples R China State Key Lab Resources & Environm Informat Syst Beijing Peoples R China Shandong Univ Sci & Technol Ctr Comp Tai An Shandong Peoples R China

ISBN: (纸本)9781467362269

Rapid surface construction for rendering with on-demand precision is essential to 3D geographic information science (GIS). high accuracy surface modeling (HASM) method can construct surface with high accuracy. However, due to its slow speed, it can't be applied to 3D GIS and real-time rendering. To meet this goal, an efficient algorithm for HASM running on GPU has been presented. Fully utilizing the GPU computing power, this method has a high efficiency and can be adapted to real-time simulation and 3D rendering. Then, we combined the high performance simulation method with an efficient rendering method, so that dynamic surface simulation and real-time rendering can be integrated in the same framework and carried out concurrently. In this framework, we simulated the coarse terrain on demand and then posted it for rendering. Preconditioned conjugate gradients method is used to solve the huge linear systems arising from HASM. As for the rendering method, we made use of state-of-the-art GPU techniques such as vertex buffer object, texture buffer so that the rendering could be carried out with high efficiency. Thus, through fully harnessing the computing power of modern GPUs with multiprocessors and many stream processors, we can execute surface modeling and rendering concurrently and efficiently. Test results show our method can construct surface dynamically and visualize it at a high frame rates and allows for high quality visualization.

关键词： GPU parallel computing high accuracy surface modeling CUDA real-time visualization

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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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A mobile agent-based architecture for on-demand processing of remote-sensing archive

A mobile agent-based architecture for on-demand processing o...

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International conference on high performance computing in the Asia-Pacific Region

作者： Yanyan Yang O.F. Rana D.W. Walker R. Williams Department of Computer Science Cardiff University Cardiff UK Oak Ridge National Laboratory Oak Ridge TN USA Center for Advanced Computing Research California Institute of Technology Pasadena CA USA

remote-sensing data about the Earth's environment is being created at an ever-increasing rate and distributed among heterogeneous remote sites. Traditional models of distributed computing are inadequate to support such complex applications, which generally involve a large quantity of data. The authors describe a mobile agent based architecture for analyzing and managing data for the Synthetic Aperture Radar Atlas (SARA) remote sensing library. A scalable and flexible architecture based on mobile agents is presented. SARA is a digital library of multi spectral remote sensing imagery of the Earth. We show how to efficiently support on-demand processing of such a remote sensing archive, using a combination of mobility and parallel computing techniques. Our architecture can support a large number of agents, where agents perform specialized roles.

关键词： remote sensing Mobile agents Computer architecture Distributed computing Software libraries Information retrieval Computer science geoscience Data analysis Radar imaging

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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

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

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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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Gobal Forest Cover Mapping using Landsat and Google Earth Engine cloud computing 8

Gobal Forest Cover Mapping using Landsat and Google Earth En...

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8th International conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Zhang, Xiaomei Long, Tengfei He, Guojin Guo, Yantao Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728121161

Nowadays, the access of Landsat data-sets and the ever-lowering costs of computing make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 meter. However, the rapid forest-covered products on a large scale, such as intercontinental or global, is still challenging. By utilizing the huge catalog of satellite imagery as well as the high-performance computing capacity of Google Earth Engine, we proposed an automated pipeline for generating 30-meter resolution global-scale forest map from time-series of Landsat images, and a novel 30-meter resolution global forest map of 2018 is released. In this paper, we describe the methods to create products of forest cover at Landsat resolutions. First, we partitioned the landscapes into sub-regions of similar forest type and spatial continuity, thus maximizing spectral differentiation, simplifying classifier model and improving classification accuracy. Then, with the existing forest cover, which come from a variety of sources, a multi-source forest/non-forest sample set was established for machine algorithm learning training. Finally, a machine learning algorithm was used to obtain samples automatically, extract the characteristics of satellite images and establish the forest / non-forest classifier models. Taking the Landsat8 images in 2018 as a case, selecting satellite image features based on the study of forest reflectance, including on-board reflectivity, the index of forest vegetation and the texture features of each hand, using established forest eco-zoning and multi-source forest / non-forest sample points, we realized automated learning and classification of forest cover for three initial zones. The accuracy verification of forest cover products in the three region was carried on two aspects: collecting verification points on high resolution satellite imagery (e.g. google earth), and cross-validating the current globally disclosed forest cover products. These two methods will illustrate the accuracy

关键词： Google Earth Engine Global Forest Eco-zoning Forest Cover Products Random Forest (RF)

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Parallel implementation of an iterative solver for atmospheric tomography 21

Parallel implementation of an iterative solver for atmospher...

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21st International conference on Computational Science and Its Applications (ICCSA)

作者： Stadler, Bernadett Biasi, Roberto Manetti, Mauro Ramlau, Ronny Johannes Kepler Univ Linz Ind Math Inst Linz Austria Microgate Bolzano Italy

ISBN: (纸本)9781665458436

Due to atmospheric turbulences the image quality of earthbound Extremely Large Telescopes (ELTs) is heavily degraded. Adaptive Optics (AO) is a technique that compensates for these quickly-varying optical aberrations. Many AO systems require the reconstruction of the turbulence profile in the atmosphere, called atmospheric tomography. The standard way of solving this problem is the so called Matrix Vector Multiplication method, i.e., the direct application of a (regularized) generalized inverse of the system operator. However, over the last years the telescope sizes have increased significantly and solving the atmospheric tomography problem in real-time become an ambitious and crucial task. Promising alternatives are iterative methods such as the Finite Element Wavelet Hybrid Algorithm (FEWHA), which is based on wavelets. Due to its efficient matrix-free representation of the underlying operators, the number of floating point operations and memory usage decrease significantly. In this paper, we focus on a parallel implementation of FEWHA on real-time hardware used for the control of large AO systems. We evaluate the computational performance of our parallel algorithm via numerical simulations for ELT-sized test configurations.

关键词： adaptive optics atmospheric tomography high performance computing iterative solvers inverse problems

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Parallel k-means Clustering of Geospatial Data Sets Using Manycore CPU Architectures 18

Parallel <i>k</i>-means Clustering of Geospatial Data Sets U...

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18th IEEE International conference on Data Mining Workshops (ICDMW)

作者： Mills, Richard Tran Sripathi, Vamsi Kumar, Jitendra Sreepathi, Sarat Hoffman, Forrest M. Hargrove, William W. Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Intel Corp Data Ctr Grp Hillsboro OR USA Oak Ridge Natl Lab Div Environm Sci POB 2008 Oak Ridge TN 37831 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN USA US Forest Serv So Res Stn USDA Asheville NC USA

ISBN: (纸本)9781538692882

The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of weather, climate, ecological, and other geoscientific data sets fused from disparate sources. Many of the standard tools used on individual workstations are impractical for the analysis and synthesis of data sets of this size;however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of parallelism available in state-of-the-art high-performance computing platforms can enable such analysis. Here, we describe pKluster, an open-source tool we have developed for accelerated k-means clustering of geospatial and geospatiotemporal data, and discuss algorithmic modifications and code optimizations we have made to enable it to effectively use parallel machines based on novel CPU architectures-such as the Intel Knights Landing Xeon Phi and Skylake Xeon processors-with many cores and hardware threads, and employing significant single instruction, multiple data (SIMD) parallelism. We outline some applications of the code in ecology and climate science contexts and present a detailed discussion of the performance of the code for one such application, LiDAR-derived vertical vegetation structure classification.

关键词： Instruction sets Parallel processing Acceleration Computer architecture Clustering algorithms geoscience

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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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Intelligent Interpretation Technology of remote sensing Images based on Deep Learning Algorithms

Intelligent Interpretation Technology of Remote Sensing Imag...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Xuelian Sui China Resource Satellite Application Center Beijing China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In recent years, high-resolution remote sensing images have been increasingly used in the field of geoscience. Therefore, how to interpret high-resolution remote sensing images with high quality is an urgent problem to be solved. This paper takes high-resolution remote sensing images as the research object, uses deep convolutional neural network (CNN) technology, conducts remote sensing image segmentation and feature extraction based on deep convolutional neural network, establishes a high-precision remote sensing image interpretation model, and improves the accuracy and computing speed of intelligent recognition of remote sensing images. This paper uses residual network (ResNet) as a learning sample to compare its performance with the classical algorithm on the same set of samples. Preliminary studies of this project have shown that the accuracy of convolutional neural networks in image analysis can reach 97.1%, and it shows obvious superiority at multiple spatial scales, and can effectively improve the efficiency of image interpretation.

关键词： Image segmentation Image recognition Accuracy Computational modeling Face recognition Feature extraction Classification algorithms Convolutional neural networks remote sensing Residual neural networks

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