COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management.

Knowledge System,Ontology,and Knowledge Graph of the Deep-Time Digital Earth(DDE): Progress and Perspective

THE USE OF KNOWLEDGE GRAPH IN NATURAL SCIENCE Knowledge graph is a field of Artificial Intelligence(AI)that aims to represent knowledge in the form of graphs,consisting of nodes and edges which represent entities and relationships between nodes respectively(Aidan et al.,2022).Although the knowledge graph was popularized recently due to use of this idea in Google’s search engine in 2012(Amit,2012),its root can be traced back to the emergence of the Semantic Web as well as earlier works in ontology(Aggarwal,2021).

A unified framework of temporal information expression in geosciences knowledge system

Time is an essential reference system for recording objects,events,and processes in the field of geosciences.There are currently various time references,such as solar calendar,geological time,and regional calendar,to represent the knowledge in different domains and regions,which subsequently entails a time conversion process required to interpret temporal information under different time references.However,the current time conversion method is limited by the application scope of existing time ontologies(e.g.,“Jurassic”is a period in geological ontology,but a point value in calendar ontology)and the reliance on experience in conversion processes.These issues restrict accurate and efficient calculation of temporal information across different time references.To address these issues,this paper proposes a Unified Time Framework(UTF)in the geosciences knowledge system.According to a systematic time element parsing from massive time references,the proposed UTF designs an independent time root node to get rid of irrelevant nodes when accessing different time types and to adapt to the time expression of different geoscience disciplines.Furthermore,this UTF carries out several designs:to ensure the accuracy of time expressions by designing quantitative relationship definitions;to enable time calculations across different time elements by designing unified time nodes and structures,and to link to the required external ontologies by designing adequate interfaces.By comparing the time conversion methods,the experiment proves the UTF greatly supports accurate and efficient calculation of temporal information across different time references in SPARQL queries.Moreover,it shows a higher and more stable performance of temporal information queries than the time conversion method.With the advent of the Big Data era in the geosciences,the UTF can be used more widely to discover new geosciences knowledge across different time references.

An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services.As a result,it has gained significant attention and become a frontier in geoscience.Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and dimensions.Therefore,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG.However,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships.To address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge.On this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships.Under the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation.This model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval.It can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

Geoscience knowledge graph in the big data era

Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.

A survey of remote sensing image classification based on CNNs

With the development of earth observation technologies,the acquired remote sensing images are increasing dramatically,and a new era of big data in remote sensing is coming.How to effectively mine these massive volumes of remote sensing data are new challenges.Deep learning provides a new approach for analyzing these remote sensing data.As one of the deep learning models,convolutional neural networks(CNNs)can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data.CNNs have achieved remarkable success in computer vision.In recent years,quite a few researchers have studied remote sensing image classification using CNNs,and CNNs can be applied to realize rapid,economical and accurate analysis and feature extraction from remote sensing data.This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification.We first briefly introduce the principles and characteristics of CNNs.We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification,available datasets for remote sensing image classification,and data augmentation techniques.Then,three typical CNN application cases in remote sensing image classification:scene classification,object detection and object segmentation are presented.We also discuss the problems and challenges of CNN-based remote sensing image classification,and propose corresponding measures and suggestions.We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data.However,data interlinking,which is the most valuable contribution of Linked Data,remains incomplete and inaccurate.This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain.According to the characteristics and roles of geospatial data in data discovery,eight elementary data characteristics are adopted as data interlinking types.These elementary characteristics are further combined to form compound and overall data interlinking types.Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively.Therefore,geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value.The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data.The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network(NSTI-GEO)and data-links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.

Geospatial data ontology:the semantic foundation of geospatial data integration and sharing

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science.However,the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing.Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines.Thus,many efforts have been made to explore ontology-based geospatial data integration and sharing.However,there is a lack of a specialized ontology that would provide a unified description for geospatial data.In this paper,with a focus on the characteristics of geospatial data,we propose a unified framework for geospatial data ontology,denoted GeoDataOnt,to establish a semantic foundation for geospatial data integration and sharing.First,we provide a characteristics hierarchy of geospatial data.Next,we analyze the semantic problems for each characteristic of geospatial data.Subsequently,we propose the general framework of GeoDataOnt,targeting these problems according to the characteristics of geospatial data.GeoDataOnt is then divided into multiple modules,and we show a detailed design and implementation for each module.Key limitations and challenges of GeoDataOnt are identified,and broad applications of GeoDataOnt are discussed.

Geospatial semantics, ontology and knowledge graphs for big Earth data

Big Data has attracted a lot of attention from governments,industries and academia,and it has been applied to a large number of fields around the world.Big Earth data refers to big data associated with the Earth sciences that is characterized as being massive,multi-source,heterogeneous,multi-temporal,multi-scalar,highly dimensional,highly complex,nonstationary,and unstructured(Guo,2017b;Guo et al.,2017a).Most big Earth data is related to a geographical location and is usually referred to as geospatial data(Lee&Kang,2015).

Numerical modeling and prediction of future response of permafrost to different climate change scenarios on the Qinghai-Tibet Plateau

Projecting the future distribution of permafrost under different climate change scenarios is essential,especially for the Qinghai–Tibet Plateau(QTP).The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs(RCP2.6,RCP4.5,RCP6.0,and RCP8.5).The simulation results show the following:(1)from now until 2070,the permafrost will experience different degrees of significant degradation under the four RCP scenarios.This will affect 25.68%,40.54%,45.95%,and 62.84%of the current permafrost area,respectively.(2)The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs.(1)In RCP2.6,the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070.(2)In RCP4.5,the rate of permafrost loss during the period 2030–2050(about 12.73%)is higher than between 2050 and 2070(about 8.33%).(3)In RCP6.0,the permafrost loss rate for the period 2030–2050(about 16.52%)is similar to that for 2050–2070(about 16.67%).(4)In RCP8.5,there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070:the rate is only about 3.70%for the first period but about 29.49%during the second.