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International Journal of Digital Earth 2021年第11期14卷 1721-1743页

作者： Christopher Scheele manzhu yu Qunying Huang Spatial Computing and Data Mining Lab Department of GeographyUniversity of Wisconsin-MadisonMadisonWIUSA Department of Geography Pennsylvania State UniversityUniversity ParkPAUSA

To find disaster relevant social media messages,current approaches utilize natural language processing methods or machine learning algorithms relying on text only,which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when ***,a disaster relevant social media message is highly sensitive to its posting location and ***,limited studies exist to explore what spatial features and the extent of how temporal,and especially spatial features can aid text *** paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets,along with the text information,for classifying disaster relevant social media *** work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data,and then used to enhance text *** deep learning-based method and commonly used machine learning algorithms,assessed the accuracy of the enhanced text-mining *** performance results of different classification models generated by various combinations of textual,spatial,and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

关键词： Spatial data science spatially enabled text mining situational awareness deep learning GeoAI spatial features

Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies

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International Journal of Digital Earth 2019年第11期12卷 1230-1247页

作者： manzhu yu Qunying Huang Han Qin Chris Scheele Chaowei Yang Department of Geography and Geoinformation Science George Mason UniveristyFairfaxVAUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA Ankura WashingtonDCUSA

Social media platforms have been contributing to disaster management during the past several *** mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes,such as caution and advice,to better understand the meaning and leverage useful information from the social media text ***,these methods are mostly event specific and difficult to generalize for cross-event *** other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future *** research examines the capability of a convolutional neural network(CNN)model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy,Harvey,and *** performance of the CNN model is compared to two traditional machine learning methods:support vector machine(SVM)and logistic regression(LR).Experiment results showed that CNN models achieved a consistently better accuracy for both single event and crossevent evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy *** indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.

关键词： Text mining deep learning hurricanes Twitter convolutional neural network situational awareness

MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

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Journal of Meteorological Research 2024年第4期38卷 693-707页

作者： Mingyue LU Zhiyu HUANG manzhu yu Hui LIU Caifen HE Chuanwei JIN Jingke ZHANG Key Laboratory of Meteorological Disaster Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Department of Geography Pennsylvania State UniversityUniversity ParkPA 16802USA ZhenhaiDistrict Meteorological Bureau Ningbo315200China Geographic Science College Nanjing University of Information Science&TechnologyNanjing 210044China School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044China

The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast *** address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 *** results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast *** issue is a common challenge in precipitation forecasting ***,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model *** model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP *** metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.

关键词： precipitation nowcasting Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM)for Precipitation Nowcasting(MGCPN) Integrated Multi-satellite Retrievals for globalprecipitation measurement(IMERG) deep learning Tibetan Plateau

Spatiotemporal event detection: a review

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International Journal of Digital Earth 2020年第12期13卷 1339-1365页

作者： manzhu yu Myra Bambacus Guido Cervone Keith Clarke Daniel Duffy Qunying Huang Jing Li Wenwen Li Zhenlong Li Qian Liu Bernd Resch Jingchao Yang Chaowei Yang Department of Geography Pennsylvania State UniversityUniversity ParkPAUSA NASA Goddard Space Flight Center NASA Goddard Space Flight CenterGreenbeltMDUSA Department of Geography University of California-Santa BarbaraSanta BarbaraCAUSA Department of Geography University of Wisconsin-MadisonMadisonWIUSA Department of Geography and the Environment University of DenverDenverCOUSA School of Geographical Sciences and Urban Planning Arizona State UniversityTempeAZUSA Department of Geography University of South CarolinaColumbiaSCUSA NSF Spatiotemporal Innovation Center&Dept.of Geography and GeoInformation Science George Mason UniversityFairfaxVAUSA Department of Geoinformatics University of SalzburgKapitelgasseSalzburgAustria Center for Geographic Analysis Harvard UniversityBostonMAUSA

The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious *** observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and *** event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant *** paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected *** novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection *** is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.

关键词： GeoAI geography and geoscience human dynamics digital earth computational challenges cloud computing internet of things

Big Earth data analytics:a survey

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Big Earth Data 2019年第2期3卷 83-107页

作者： Chaowei Yang manzhu yu yun Li Fei Hu Yongyao Jiang Qian Liu Dexuan Sha Mengchao Xu Juan Gu NSF Spatiotemporal Innovation Center George Mason UniversityFairfaxVA 22030USA Center for OpenSource Data and AI Technologies IBMSan FranciscoCAUSA Machine Learning at ESRI Ltd RedlandsCAUSA Department of fundamental Geoinformatics Beijing Institute of Surveying and MappingBeijngChina

Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other *** data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and *** the past years,new technologies(such as cloud computing,big data and artificial intelligence)have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically *** paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing *** first introduce the concepts of big Earth *** architecture,various functionalities,and supporting modules are then reviewed from a generic methodology *** methods supporting the functionalities are surveyed and analyzed in the context of different *** driven questions are exemplified through cutting-edge Earth science researches and applications.A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

关键词： Geospatial industry geospatial analytics policy makers data scientist data system spatiotemporal data

Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan manzhu yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big Data Earth system emergency geospatial sciences epidemics applications

聚合物—无机层状氧化物纳米复合材料的研究进展

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陶瓷学报 2003年第1期24卷 54-57页

作者：柯满竹俞康泰武汉理工大学

本文以MoO3、V2 O5等无机层状氧化物为研究基础 ,评述了聚合物—无机层状氧化物纳米复合材料的制备方法 ,分析了其结构特征 ,研究表明这类纳米复合材料仍为层状结构 ,且层间距增大 ;同时分析了其性能特征及其在电致变电材料和阴极材料... 详细信息

本文以MoO3、V2 O5等无机层状氧化物为研究基础 ,评述了聚合物—无机层状氧化物纳米复合材料的制备方法 ,分析了其结构特征 ,研究表明这类纳米复合材料仍为层状结构 ,且层间距增大 ;同时分析了其性能特征及其在电致变电材料和阴极材料中的主要应用 ,并指出目前该研究领域的前沿问题 ,最后展望了这类复合材料的发展前景。

关键词：聚合物-无机层状氧化物纳米复合材料研究进展电致变电材料阴极材料

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手掌外科微创术发展概况

中国蒙医药（蒙） 2021年第2期16卷 45-49页

作者：石桩于满柱图布新不详

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高光谱影像混合像元分解比较研究

地理空间信息 2011年第3期9卷 3-5,8页

作者：刘涛严海英于曼竹新疆维吾尔自治区第二测绘院新疆乌鲁木齐830002 武汉大学遥感信息工程学院湖北武汉430079

遥感混合像元分解,作为一种遥感分类与制图的方法,具有其独特的优势。利用新疆阜康地区的Hyperion遥感影像,在ENVI/IDL软件运行环境下,分别采用沙漏算法,SMACC算法和体积法进行端元提取,并对3种方法进行了比较分析,从中选择符合实际的... 详细信息

遥感混合像元分解,作为一种遥感分类与制图的方法,具有其独特的优势。利用新疆阜康地区的Hyperion遥感影像,在ENVI/IDL软件运行环境下,分别采用沙漏算法,SMACC算法和体积法进行端元提取,并对3种方法进行了比较分析,从中选择符合实际的沙漏算法提取的端元,作为最终端元。在此基础上,分别运用最小二乘法、OSP算法混合像元分解,最后统计与比较各种分解方法。实验结果表明:在该区沙漏算法提取的端元精度较高,基于全约束性最小二乘法解混结果符合实际情况。

关键词：混合像元端元混合像元分解

Symmetry-enforced three-dimensional Dirac phononic crystals

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Light(Science & Applications) 2020年第1期9卷 1641-1646页

作者： Xiangxi Cai Liping Ye Chunyin Qiu Meng Xiao Rui yu manzhu Ke Zhengyou Liu Key Laboratory of Artificial Micro-Structures and Nano-Structures of Ministry of Education and School of Physics and Technology Wuhan University430072 WuhanChina Institute for Advanced Studies Wuhan University430072 WuhanChina

Dirac semimetals,the materials featuring fourfold degenerate Dirac points,are critical states of topologically distinct *** gapless topological states have been accomplished by a band-inversion mechanism,in which the Dirac points can be annihilated pairwise by perturbations without changing the symmetry of the ***,we report an experimental observation of Dirac points that are enforced completely by the crystal symmetry using a nonsymmorphic three-dimensional phononic ***,our Dirac phononic crystal hosts four spiral topological surface states,in which the surface states of opposite helicities intersect gaplessly along certain momentum lines,as confirmed by additional surface *** novel Dirac system may release new opportunities for studying elusive(pseudo)and offer a unique prototype platform for acoustic applications.

关键词： Dirac topological forced