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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Li, Yan Han, Shi-Yuan Shen, Yuan Liu, Zhen Dong, Ji-Wen Xu, Tao University of Jinan Jinan250022 China China High-Resolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

The detection of traffic element in remote sensing imagesplays an important role in the construction of traffic infrastructure, and can provide dynamic monitoring and quality evaluation for traffic construction projects. The convolutional neural network (CNN)-based method has achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, the existing dataset can only be used for object detection, and the sample size and diversity need to be improved. These problems have brought challenges to the improvement and application of existing traffic element detection methods. This paper proposes a large-scale highresolution traffic element dataset based on multi-method detection analysis, and validates it based on the CNN-based object detection method. We screened more than 100 satellite images from high-resolution satellite images with a spatial resolution of 0.8 m. Manually labeled 5 categories and 100,000 traffic element samples. In the experiment, three CNN-based detection methods were used as the experimental baseline method. The experimental results proved effectiveness of the UJN-traffic traffic element dataset and provided a useful reference for traffic construction engineering and management. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal Dataset with CNN Based Semantic Segmentation Analysis 7th

UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Author Correction: Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

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Nature cell biology 2024年第5期26卷 839页

作者： Sanjay Jain Liming Pei Jeffrey M Spraggins Michael Angelo James P Carson Nils Gehlenborg Fiona Ginty Joana P Gonçalves James S Hagood John W Hickey Neil L Kelleher Louise C Laurent Shin Lin Yiing Lin Huiping Liu Alexandra Naba Ernesto S Nakayasu Wei-Jun Qian Andrea Radtke Paul Robson Brent R Stockwell Raf Van de Plas Ioannis S Vlachos Mowei Zhou Katy Börner Michael P Snyder Department of Medicine Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pediatrics Washington University School of Medicine St Louis MO USA. sanjayjain@wustl.edu. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. sanjayjain@wustl.edu. Center for Mitochondrial and Epigenomic Medicine Department of Pathology and Laboratory Medicine Children's Hospital of Philadelphia and Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. lpei@pennmedicine.upenn.edu. Department of Cell and Developmental Biology and the Mass Spectrometry Research Center Vanderbilt University School of Medicine Nashville TN USA. jeff.spraggins@vanderbilt.edu. Department of Pathology Stanford School of Medicine Stanford CA USA. Texas Advanced Computing Center University of Texas at Austin Austin TX USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. GE Research Center Niskayuna NY USA. Department of Intelligent Systems Delft University of Technology Delft Netherlands. Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Microbiology and Immunology Stanford University Stanford CA USA. Departments of Medicine Chemistry and Molecular Biosciences Northwestern University Evanston IL USA. Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA. Division of Cardiology University of Washington School of Medicine Seattle WA USA. Department of Surgery Washington University School of Medicine St Louis MO USA. Departments of Pharmacology Medicine (Hematology and Oncology) Lurie Cancer Center Northwestern University Feinberg School of Medicine Chicago IL USA. Department of Physiology and Biophysics University of Illinois at Chicago Chicago IL USA. Biological Sciences Division

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Self-Triggered Distributed Optimization for Multi-Agent Systems with Periodic Sampling Schemes

Self-Triggered Distributed Optimization for Multi-Agent Syst...

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Chinese Automation Congress (CAC)

作者： Shuling Li Wu Ai Jian Wu College of Science Center for Data analysis and Algorithm Technology Guilin University of Technology Guilin P. R. China College of Science Guilin University of Technology Guangxi Key Laboratory of Spatial Information and Geomatics Guilin P. R. China Perception and Computing of Anhui University Key Laboratory of Intelligent Province Anqing P. R. China

This work is the first to consider the self-triggered distributed optimization problem for multi-agent systems with periodic sampling communication. Each agent computes its next update time instance at the previous time. The self-triggered distributed protocols are proposed to reduce the load of in-network communication. It is proved that these self-triggered protocol achieves optimal consensus exponentially under a undirected and connected network topology.

关键词： Multi-agent systems Cost function Protocols Eigenvalues and eigenfunctions Geomagnetism Network topology

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并行计算环境下三种空间计算域划分算法的设计与比较-以沙尘暴数值模拟为例

并行计算环境下三种空间计算域划分算法的设计与比较-以沙尘暴数...

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中国地理信息科学2014学术年会

作者：桂志鹏姜云峰杨超伟武汉大学遥感信息工程学院武汉430079 Center of Intelligent Spatial Computing for Water/Energy Science George Maso Center of Intelligent Spatial Computing for Water/Energy Science George Mason University Fairfax

作为一种自然天气过程,沙尘暴对环境、人类健康及社会财产都有严重的负面影响.随着全球气候的持续变化和人为因素,沙尘暴的频率和强度已经在过去几十年内发生了显著提升.作为沙尘暴的频发区域,我国北方地区,近十年来几乎每年冬春两季都... 详细信息

作为一种自然天气过程,沙尘暴对环境、人类健康及社会财产都有严重的负面影响.随着全球气候的持续变化和人为因素,沙尘暴的频率和强度已经在过去几十年内发生了显著提升.作为沙尘暴的频发区域,我国北方地区,近十年来几乎每年冬春两季都会发生若干次强沙尘暴天气,对人民的交通出行、工作和生活带来诸多不便,并已经严重危害了人民的健康和安全. 针对这NP Hard问题，本文提出三种不同的划分方法，分别是基于二次规划的最优化方法(Quadratic Programming，QP)、一种K-Means聚类算法和Kernighan-Lin节点置换算法结合的三阶段启发式计算域划分算法(K&K)和基于自动种子区域生长的算法(AutomaticSeeded Region Growing，ASRG)，并针对求解效率和效果进行了深入的比较。针对求解效率，本文比较了三种算法在不同问题规模下的算法求解时间。针对求解效率，本文从子计算域上数据块的均衡性、子计算域上跨域通讯量的均衡性以及子计算域划分过程中产生的总通讯录三个方面进行度量和评价，并针对不同的问题规划、区域形状进行了深入的比较。实验表明三种算法有各自不同的适用场合，但总的来说K&K算法不仅有较好的收敛速度，能够胜任大计算域（网格节点数量大）划分的问题;同时有良好计算效果，可以保证较好的划分形态。本文提出的K&K算法显著降低了计算节点之间的通讯成本、平衡了计算负载并降级了总计算时间（针对不同计算节点个数和数据块粒度的实验中，数值模拟过程提速最高可达20％-80%）。因此该算法显示了其在沙尘暴数值模拟过程中的实用价值;同时，在涉及计算域划分的同类计算和通讯密集型科学计算中，该算法也具有广泛的潜在应用空间。

关键词：沙尘暴天气预测计算域划分过程最优化方法 K-Means聚类算法 Kernighan-Lin节点置换算法

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Redefining the possibility of digital Earth and geosciences with spatial cloud computing

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International Journal of Digital Earth 2013年第4期6卷 297-312页

作者： Chaowei Yang Yan Xu Douglas Nebert Center of Intelligent Spatial Computing for Water/Energy Sciences George Mason UniversityFairfaxVAUSA Microsoft Research RedmondWAUSA Federal Geographic Data Committee RestonVAUSA

Global challenges(such as economy and natural hazards)and technology advancements have triggered international leaders and organizations to rethink geosciences and Digital Earth in the new *** next generation visions pose grand challenges for infrastructure,especially computing *** gradual establishment of cloud computing as a primary infrastructure provides new capabilities to meet the *** paper reviews research conducted using cloud computing to address geoscience and Digital Earth needs within the context of an integrated Earth *** also introduce the five papers selected through a rigorous review process as exemplar research in using cloud capabilities to address the *** literature and research demonstrate that spatial cloud computing provides unprecedented new capabilities to enable Digital Earth and geosciences in the twenty-first century in several aspects:(1)virtually unlimited computing power for addressing big data storage,sharing,processing,and knowledge discovering challenges,(2)elastic,flexible,and easy-to-use computing infrastructure to facilitate the building of the next generation geospatial cyberin-frastructure,CyberGIS,CloudGIS,and Digital Earth,(3)seamless integration environment that enables mashing up observation,data,models,problems,and citizens,(4)research opportunities triggered by global challenges that may lead to breakthroughs in relevant fields including infrastructure building,GIScience,computer science,and geosciences,and(5)collaboration supported by cloud computing and across science domains,agencies,countries to collectively address global challenges from policy,management,system engineering,acquisition,and operation aspects.

关键词： EarthCube CloudGIS eScience Earthscience interoperability

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A high performance web-based system for analyzing and visualizing spatiotemporal data for climate studies

A high performance web-based system for analyzing and visual...

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12th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2013

作者： Li, Zhenlong Yang, Chaowei Sun, Min Li, Jing Xu, Chen Huang, Qunying Liu, Kai Center of Intelligent Spatial Computing for Water/Energy Sciences George Mason University Fairfax VA 22030-4444 United States

ISBN: (纸本)9783642370861

Large amount of data are produced at different spatiotemporal scales by many sensors observing Earth and model simulations. Although advancements of contemporary technologies provide better solutions to access the spatiotemporal data, it is still a big challenge for researchers to easily extract information and knowledge from the data due to the data complexities of high dimensions, heterogeneity, distribution, large amount and frequently updating. This is especially true in climate studies, because climate data with coverage of the entire Earth and a long time period (such as 200 years) are often required to extract useful climate change information and patterns. A well-developed online visual analytical system has the potential to provide an efficient mechanism to bridge this gap. Using performance improving techniques for an online visual analytical system, we researched and developed a high performance Web-based system for spatiotemporal data visual analytics includes the following components: 1) a spatial Data Registration center for managing the big spatiotemporal data and enabling researchers to focus on analyses without worrying about data related issues such as format, management and storage;2) a workflow for pre-generating and caching frequently requested data to reduce the server response time;and 3) a technique of "single data fetch, multiple analyses" to reduce both server response time and client response time;Finally, we demonstrate the effectiveness of the prototype through a few use cases. © 2013 Springer-Verlag.

关键词： Big data

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Utilize cloud computing to support dust storm forecasting

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International Journal of Digital Earth 2013年第4期6卷 338-355页

作者： Qunying Huang Chaowei Yang Karl Benedict Songqing Chen Abdelmounaam Rezgui Jibo Xie Joint Center for Intelligent Spatial Computing Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxVAUSA Earth Data Analysis Center(EDAC) University of New MexicoAlbuquerqueNMUSA Department of Computer Science George Mason UniversityFairfaxVAUSA Center for Earth Observation and Digital Earth BeijingChina

The simulations and potential forecasting of dust storms are of significant interest to public health and environment *** storms have interannual variabilities and are typical disruptive *** computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and *** the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance *** paper examines the viability for cloud computing to support dust storm *** a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

关键词： spatial cloud computing CyberGIS cloud GIS loosely coupled nested model Amazon EC2

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Efficient lidar point cloud data managing and processing in a Hadoop-based distributed framework 2

Efficient lidar point cloud data managing and processing in ...

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2nd International Symposium on Spatiotemporal computing, ISSC 2017

作者： Wang, C. Hu, F. Sha, D. Han, X. Hainan Geomatics Center National Administration of Surveying Mapping and Geoinformation of China Haikou Hainan570203 China Department of Geography and GeoInformation Science Center for Intelligent Spatial Computing George Mason University FairfaxVA22030-4444 United States

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mappingï1/4city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop's storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data. © Authors 2017.

关键词： Distributed computing GIS Hadoop HDFS Lidar data MapReduce PCL Point cloud

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Data-intensive spatial Indexing on the Clouds

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Procedia Computer Science 2013年 18卷 2615-2618页

作者： Abdelmounaam Rezgui Zaki Malik Jizhe Xia Kai Liu Chaowei Yang Department of Computer Science & Engineering New Mexico Tech Socorro NM USA Center for Intelligent Spatial Computing George Mason University Fairfax VA USA

In this paper, we investigate the potential of cloud computing for data-intensive spatial indexing. We assess the benefits in performance and cost. Specifically, we consider a data- and compute-intensive spatial application, namely, the construction of very large R-tree spatial indexes. We selected this application because of its high computing and memory requirements. We implemented this application and deployed it on various types of cloud configurations. We report our findings and provide insights useful when considering cloud computing for data- and compute-intensive spatial applications.

关键词： Cloud computing Data-intensive spatial indexing R-Trees

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