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| 1 | Big Earth data analytics:a survey显示文摘Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other sources.The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and applications.In 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 intractable.This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain.We first introduce the concepts of big Earth data.The architecture,various functionalities,and supporting modules are then reviewed from a generic methodology aspect.Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools.The 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. | Chaowei Yang Manzhu Yu Yun Li Fei Hu Yongyao Jiang Qian Liu Dexuan Sha Mengchao Xu Juan Gu | 2019 | Big Earth Data2019，3，2： | 3 |
| 2 | Symmetry-enforced three-dimensional Dirac phononic crystals显示文摘Dirac semimetals,the materials featuring fourfold degenerate Dirac points,are critical states of topologically distinct phases.Such 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 system.Here,we report an experimental observation of Dirac points that are enforced completely by the crystal symmetry using a nonsymmorphic three-dimensional phononic crystal.Intriguingly,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 measurements.The novel Dirac system may release new opportunities for studying elusive(pseudo)and offer a unique prototype platform for acoustic applications. | Xiangxi Cai Liping Ye Chunyin Qiu Meng Xiao Rui Yu Manzhu Ke Zhengyou Liu | 2020 | Light(Science & Applications)2020，9，1： | 2 |
| 3 | Spatiotemporal event detection: a review显示文摘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 diseases.The 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 crime.Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena.This paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected events.The 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 research.Guidance 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. | 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 | 2020 | International Journal of Digital Earth2020，13，12： | 2 |
| 4 | Taking the pulse of COVID-19:a spatiotemporal perspective显示文摘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 U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In 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 citizens.This 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. | 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 | 2020 | International Journal of Digital Earth2020，13，10： | 2 |
| 5 | Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies显示文摘Social media platforms have been contributing to disaster management during the past several years.Text 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 content.However,these methods are mostly event specific and difficult to generalize for cross-event classifications.In other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event.This 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 Irma.The 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 results.This 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. | Manzhu Yu Qunying Huang Han Qin Chris Scheele Chaowei Yang | 2019 | International Journal of Digital Earth2019，12，11： | 1 |
| 6 | Geographic context-aware text mining:enhance social media message classification for situational awareness by integrating spatial and temporal features显示文摘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 posted.Meanwhile,a disaster relevant social media message is highly sensitive to its posting location and time.However,limited studies exist to explore what spatial features and the extent of how temporal,and especially spatial features can aid text classification.This 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 posts.This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data,and then used to enhance text mining.The deep learning-based method and commonly used machine learning algorithms,assessed the accuracy of the enhanced text-mining method.The 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. | Christopher Scheele Manzhu Yu Qunying Huang | 2021 | International Journal of Digital Earth2021，14，11： | 1 |
| 7 | Genetic characterization and pathogenicity of a reassortant Eurasian avian-like H1N1 swine influenza virus containing an internal gene cassette from 2009 pandemic H1N1 virus显示文摘Dear editor,Swine influenza virus(SIV)is a member of the Orthomyxoviridae family,influenza A virus genus,which can cause the swine influenza—an acute and highly contagious respiratory disease in pigs(Brown,2000;Kothalawala et al.,2006).The SIV was first observed in 1918 in the United States and the progenitor of the SIV was the H1N1 influenza virus which caused the Spanish influenza pandemic of 1918(Shope,1931). | Shuaiyong Wang Manzhu Wang Lingxue Yu Juan Wang Jiecong Yan Xinli Rong Yanjun Zhou Tongling Shan Wu Tong Guoxin Li Hao Zheng Guangzhi Tong Hai Yu | 2022 | Virologica Sinica2022，37，4： | 0 |
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