Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to inc...Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.展开更多
Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale buildin...Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.展开更多
With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a co...With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.展开更多
Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’...Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’Sus-tainable Development Goals(SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education ser-vices in China's Mainland in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7%and 92.5%of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20%of counties(by GDP)from access to hospital and school services reached approximately 80%of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibil-ity metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.展开更多
Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate t...Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making.However,real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research,we demonstrate how we obtain historical time-series and real-time data from sensors oper-ated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne,UK.In the study,we assess the impact of rainfall on traffic volume and travel time,and the cascading impacts during a storm event in Newcastle during early October 2021.We also estimate the economic cost of the storm,with regards to transport disruption,as the cost of travel,using the“value of time”based on Department for Transport guidelines(2021).Using spatial-temporal analysis,we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm.We identified increases in travel times of up to 600%and decreases in traffic volume of up to 100%when compared to historical data.Further,we assessed cascading impacts at important traffic locations and their broader implications for city areas.We estimated that the storm’s economic impact on one sensor location increased by up to 370%of the reference value.By analysing historical and real-time data,we detected and explained patterns in the data that would have remained uncovered if they had been examined individually.The combination of different data sources,such as traffic and weather,helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.展开更多
This article assesses the feasibility of generating the geospatial data from a national classification standard.In this case,the National Standardization Agency(Badan Standardisasi Nasional)of Indonesia created and pu...This article assesses the feasibility of generating the geospatial data from a national classification standard.In this case,the National Standardization Agency(Badan Standardisasi Nasional)of Indonesia created and published a national seabed cover classification standard called SNI 7987–2014 but has not developed corresponding geospatial data.Geospatial data on seabed cover can be generated by integrating related thematic data,such as those on seafloor surficial sediments,coastal ecosystems,and coastal infrastructure.With consideration for these issues,this research evaluated the feasibility of using SNI 7987–2014 as a means of generating seabed cover geospatial data at scales of 1:250,000 and 1:50,000.To this end,the documentation accompanying the standard was evaluated via descriptive quantitative analysis through weighted scoring,and logical testing,after which overlay,feature selection based on the scored method and remote sensing analysis were carried out to develop the geospatial data prototypes.Results showed that the feasibility levels of using the prototypes for generating data at scales of 1:250,000 and 1:50,000 were 87.5%and 86.5%,respectively,indicating that SNI 7987–2014 can be fully used as the basis for generating geospatial data on seabed cover.展开更多
Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, res...Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.展开更多
The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technol...The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and users.While previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial data.Employing a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced initiatives.This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection.Furthermore,it addresses salient challenges,encompassing data quality,inherent biases,and ethical dimensions.We contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.展开更多
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 Linke...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.展开更多
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 geospatia...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.展开更多
Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and propert...Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.展开更多
The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created.However,challenges remain with connecting ...The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created.However,challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist.The objective of this paper is to present a publically available Geospatial Search Engine(GSE)that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data.The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services.A procedure runs daily to update map server layers and metadata,and to eliminate servers that go offline.The GSE supports Web Map Services,ArcGIS services,and websites that have geospatial data for download.We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services.While enhancements to our GSE and to web crawler technology in general lie ahead,our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data.展开更多
There is a critical need to develop a means for fast,task-driven discovery of geospatial data found in geoportals.Existing geoportals,however,only provide metadata-based means for discovery,with little support for tas...There is a critical need to develop a means for fast,task-driven discovery of geospatial data found in geoportals.Existing geoportals,however,only provide metadata-based means for discovery,with little support for task-driven discovery,especially when considering spatial–temporal awareness.To address this gap,this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery(CBR-GDD)method and implementation that accesses geospatial data by tasks.The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness,thus providing solutions based on past tasks.The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals:ontology-enhanced knowledge base,similarity assessment model,and case retrieval nets.A set of experiments and case studies validate the CBR-GDD approach and application,and demonstrate its efficiency.展开更多
In recent years,geographical information systems have been employed in a wide variety of application domains,and as a result many research efforts are being devoted to those upcoming problems.Geospatial data security,...In recent years,geographical information systems have been employed in a wide variety of application domains,and as a result many research efforts are being devoted to those upcoming problems.Geospatial data security,especially access control,has attracted increased research interests within the academic community.The tendency towards sharing and interoperability of geospatial data and applications makes it common to acquire and integrate geospatial data from multiple organisations to accomplish a complex task.Meanwhile,many organisations have the requirement for securing access to possessed sensitive or proprietary geospatial data.In this heterogeneous and distributed environment,consistent access control functionality is crucial to promote controlled accessibility.As an extension of general access control mechanisms in the IT domain,the mechanism for geospatial data access control has its own requirements and characteristics of granularity and geospatial logic.In this paper,we address several fundamental aspects concerning the design and implementation of an access control system for geospatial data,including the classification,requirements,authorisation models,storage structures and management approaches for authorisation rules,matching and decision-making algorithms between authorisation rules and access requests,and its policy enforcement mechanisms.This paper also presents a system framework for realising access control functionality for geospatial data,and explain access control procedures in detail.展开更多
The development of technology and the demands of groups of interest for standardized digital geospatial information are increasing daily.The necessity for referred geospatial information,according to a Referencing Coo...The development of technology and the demands of groups of interest for standardized digital geospatial information are increasing daily.The necessity for referred geospatial information,according to a Referencing Coordinating System and European Standards,through a national GIS(Geographic Information System)system,requires a decision making of national and institutional importance.ASIG(State Authority for Geospatial Information)is the institution that administrates,implements,and maintains the NSDI(National Spatial Data Infrastructure).It is calculated that 80%of decision-making by public or private institutions uses geospatial data with a well-organized structure that enables efficiency.Thus,standardization of geospatial data by topic is one of the main objectives of implementing the NSDI in Albania.This is a complex task for the standard and the harmonization of geospatial data,which can be a good opportunity for professional awareness.This study shows in detail the methodology for the creation and implementation of data specification for geospatial information in Albania on the theme:Geology,adoption of the technical specification of the INSPIRE directive as well as the importance of ASIG as an institution that builds and maintains NSDI in Albania.展开更多
With the rapid growth of the Internet,the copyright protection problem occurs frequently,and unauthorized copying and distributing of geospatial data threaten the investments of data producers.Digital watermarking is ...With the rapid growth of the Internet,the copyright protection problem occurs frequently,and unauthorized copying and distributing of geospatial data threaten the investments of data producers.Digital watermarking is a possible solution to solve this issue.However,watermarking causes modifications in the original data resulting in distortion and affects accuracy,which is very important to geospatial vector data.This article provides distortion assessment of watermarked geospatial data using wavelet-based invisible watermarking.Eight wavelets at different wavelet decomposition levels are used for accuracy evaluation with the help of error measures such as maximum error and mean square error.Normalized correlation is used as a similarity index between original and extracted watermark.It is observed that the increase in the strength of embedding increases visual degradation.Haar wavelet outperforms the other wavelets,and the third wavelet decomposition level is proved to be optimal level for watermarking.展开更多
For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services a...For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services accelerate geospatial capture,coordination,and intelligence in unprecedented ways.Underlying the digital transformation of industry and society is the fusion of the physical and digital worlds-‘perceptality’-where geospatial perception and reality merge.This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application.Applications of sensors,robotics,cameras,machine learning,encryption,cloud computing and other software,and hardware intelligence are converging,enabling new ways for organizations and their equipment to perceive and capture reality.Meanwhile,demands for performance,reliability,and security are pushing compute‘to the edge’where real-time processing and coordination are vital.Big data place new restraints on economics,as pressures abound to actually use these data,both in real-time and for longer term strategic analysis and decision-making.These challenges require orchestration between information technology(IT)and operational technology(OT)and synchronization of diverse systems,data-sets,devices,environments,workflows,and people.展开更多
The National Bureau of Surveying and Mapping of China has planned to speed up the development of spatial data infrastructure (SDI) in the coming few years.This SDI consists of four types of digital products,i.e.,digit...The National Bureau of Surveying and Mapping of China has planned to speed up the development of spatial data infrastructure (SDI) in the coming few years.This SDI consists of four types of digital products,i.e.,digital orthophotos,digital elevation models, digital line graphs and digital raster graphs.For the DEM,a scheme for the database building and updating of 1∶10 000 digital elevation models has been proposed and some experimental tests have also been accomplished.This paper describes the theoretical (and/or technical) background and reports some of the experimental results to support the scheme.Various aspects of the scheme such as accuracy,data sources,data sampling,spatial resolution,terrain modeling,data organization,etc are discussed.展开更多
The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the ...The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the hydrochemi-cal dispersion and geospatial correlation of atmospheric petroleum hydrocar-bons(TPH)deposited through rainfall within selected locations in Rivers State,Nigeria.Thirty-four rainwater samples were collected from seven distinct sub-regional study locations:Obigbo,Komkom,Obiama,Okoloma,Egberu,Umu Agbai,and Obete.Hydrochemical analyses were conducted using gas chroma-tography-flame ionization detection(GC-FID)to quantify total petroleum hy-drocarbons(TPH),with detailed compositional profiling of aliphatic hydro-carbons(C8-C40)and polycyclic aromatic hydrocarbons(PAHs).The disper-sion analysis employed Hexbin density mapping,Contour visualization,and spatial interpolation techniques to delineate pollution hotspots,revealing sig-nificant contamination gradients across the study region.The correlation ma-trix assessed interrelationships between hydrocarbon fractions and geographic positioning,identifying strong positive correlations(r>0.9)between TPH and total aliphatic hydrocarbons(TAH),suggesting transportation and industrial emissions as primary sources.PAHs exhibited localized concentration spikes,particularly near gas flaring zones and commercial hubs,implicating fossil fuel combustion,industrial activities,and long-range pollutant transport as domi-nant contamination mechanisms.Geospatial analysis indicates higher hydro-carbon deposition in the Western and Northern regions,with Obigbo and Okoloma experiencing the most significant contamination.The study high-lights rainfall as a key vector for atmospheric petrochemical deposition,with implications for water quality,ecosystem health,and human exposure risks.These findings emphasize the need for stricter environmental monitoring,reg-ulatory enforcement of industrial emissions,and strategic efforts to mitigate hydrocarbon pollution in petroleum-producing regions.展开更多
坐标转换是空间地理数据应用的常用功能,由于部门及应用场景不同,不同坐标系成果的相互转换工作将长期存在。目前的坐标转换不仅需要脱离ArcGIS和AutoCAD等软件,还要实现主流矢量数据和栅格数据的坐标转换。为此提出了基于GDAL(geospati...坐标转换是空间地理数据应用的常用功能,由于部门及应用场景不同,不同坐标系成果的相互转换工作将长期存在。目前的坐标转换不仅需要脱离ArcGIS和AutoCAD等软件,还要实现主流矢量数据和栅格数据的坐标转换。为此提出了基于GDAL(geospatial data abstraction library)和Teigha库底层开发的通用坐标转换方法,给出了系统架构,实现了参数管理、参数计算、坐标转换、数据读写等功能。该方法大大提高了转换效率和保密性。实践表明,该系统转换结果正确可靠。展开更多
基金supported by the National Natural Science Foundation of China(No.62002144)Ministry of Education Chunhui Plan Research Project(Nos.202200345,HZKY20220125).
文摘Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.
基金supported by the National Science Foundation[grant numbers 1854502 and 1855902]Publication was made possible in part by support from the HKU Libraries Open Access Author Fund sponsored by the HKU Libraries.USDA is an equal opportunity provider and employer.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S.Department of Agriculture.
文摘Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.
基金supported by the project of Beijing Municipal Science and Technology Commission and Science and Technology Innovation Base of Cultivating and Developing Engineering[grant number Z161100005016069]the National High Technology Research and Development Program[grant number 2013AA12A303].
文摘With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.
基金This work was supported by the National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.41725006).
文摘Growing attention has been directed to the use of satellite imagery and open geospatial data to understand large-scale sustainable development outcomes.Health and education are critical domains of the Unites Nations’Sus-tainable Development Goals(SDGs),yet existing research on the accessibility of corresponding services focused mainly on detailed but small-scale studies.This means that such studies lack accessibility metrics for large-scale quantitative evaluations.To address this deficiency,we evaluated the accessibility of health and education ser-vices in China's Mainland in 2021 using point-of-interest data,OpenStreetMap road data,land cover data,and WorldPop spatial demographic data.The accessibility metrics used were the least time costs of reaching hospital and school services and population coverage with a time cost of less than 1 h.On the basis of the road network and land cover information,the overall average time costs of reaching hospital and school were 20 and 22 min,respectively.In terms of population coverage,94.7%and 92.5%of the population in China has a time cost of less than 1 h in obtaining hospital and school services,respectively.Counties with low accessibility to hospitals and schools were highly coupled with poor areas and ecological function regions,with the time cost incurred in these areas being more than twice that experienced in non-poor and non-ecological areas.Furthermore,the cumulative time cost incurred by the bottom 20%of counties(by GDP)from access to hospital and school services reached approximately 80%of the national total.Low-GDP counties were compelled to suffer disproportionately increased time costs to acquire health and education services compared with high-GDP counties.The accessibil-ity metrics proposed in this study are highly related to SDGs 3 and 4,and they can serve as auxiliary data that can be used to enhance the evaluation of SDG outcomes.The analysis of the uneven distribution of health and education services in China can help identify areas with backward public services and may contribute to targeted and efficient policy interventions.
基金supported by the United Kingdom’s Engineering and Physical Sciences Research Council(EPSRC)under grant number EP/S023577/1,and Ordnance Survey of Great Britain.
文摘Extreme weather conditions can adversely impact transport networks and driver behaviour,leading to variations in traffic volumes and travel times and increased accident rates.Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making.However,real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research,we demonstrate how we obtain historical time-series and real-time data from sensors oper-ated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne,UK.In the study,we assess the impact of rainfall on traffic volume and travel time,and the cascading impacts during a storm event in Newcastle during early October 2021.We also estimate the economic cost of the storm,with regards to transport disruption,as the cost of travel,using the“value of time”based on Department for Transport guidelines(2021).Using spatial-temporal analysis,we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm.We identified increases in travel times of up to 600%and decreases in traffic volume of up to 100%when compared to historical data.Further,we assessed cascading impacts at important traffic locations and their broader implications for city areas.We estimated that the storm’s economic impact on one sensor location increased by up to 370%of the reference value.By analysing historical and real-time data,we detected and explained patterns in the data that would have remained uncovered if they had been examined individually.The combination of different data sources,such as traffic and weather,helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.
文摘This article assesses the feasibility of generating the geospatial data from a national classification standard.In this case,the National Standardization Agency(Badan Standardisasi Nasional)of Indonesia created and published a national seabed cover classification standard called SNI 7987–2014 but has not developed corresponding geospatial data.Geospatial data on seabed cover can be generated by integrating related thematic data,such as those on seafloor surficial sediments,coastal ecosystems,and coastal infrastructure.With consideration for these issues,this research evaluated the feasibility of using SNI 7987–2014 as a means of generating seabed cover geospatial data at scales of 1:250,000 and 1:50,000.To this end,the documentation accompanying the standard was evaluated via descriptive quantitative analysis through weighted scoring,and logical testing,after which overlay,feature selection based on the scored method and remote sensing analysis were carried out to develop the geospatial data prototypes.Results showed that the feasibility levels of using the prototypes for generating data at scales of 1:250,000 and 1:50,000 were 87.5%and 86.5%,respectively,indicating that SNI 7987–2014 can be fully used as the basis for generating geospatial data on seabed cover.
文摘Climate change effects have had negative effects on most farmers, both small and large-scale, with weather patterns increasingly becoming unpredictable, such that farmers are unable to plan well for their farming, resulting in reduced harvests and sometimes losses for farmers. Better availability of information such as weather patterns, suitable crops, nutrient requirements based on soil types and conditions would greatly alleviate these challenges. While geospatial information is being developed and improved continuously by researchers, its accessibility and use by the counties has not been established and cannot be identified as contributing to better crop production outcomes. The aim of this study, therefore, was to assess the awareness and status of geospatial data availability and use for crop production, and the level of the relevant capacities, both human and infrastructural, in selected Counties of Kenya. A survey was conducted in the four counties of Vihiga, Kilifi, Wajir and Nyeri and key informant interviews were conducted with both management and technical County Agricultural Officers, as well as sub-county agricultural extension officers. From the results of the survey, out of the four counties, only one has adequate infrastructure in terms of hard-ware, software and connectivity to conduct useful geospatial data acquisition and processing. While most indicated awareness of the existence of geospatial data, limited resources, low skills and knowledge have restricted any meaningful sourcing of and access to data, with only 38% moderately or highly skilled in acquisition, 48% in processing and 57% in interpretation and use of geospatial data. The study concludes that moderate skills and capacities available within the counties have considerable potential to make use the available geospatial data to inform farmers accordingly and improve their farming outcomes.
基金supported by the Faculty Startup Fund of the College of Arts and Sciences at Emory University.
文摘The transformation from authoritative to user-generated data landscapes has garnered considerable attention,notably with the proliferation of crowdsourced geospatial data.Facilitated by advancements in digital technology and high-speed communication,this paradigm shift has democratized data collection,obliterating traditional barriers between data producers and users.While previous literature has compartmentalized this subject into distinct platforms and application domains,this review offers a holistic examination of crowdsourced geospatial data.Employing a narrative review approach due to the interdisciplinary nature of the topic,we investigate both human and Earth observations through crowdsourced initiatives.This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection.Furthermore,it addresses salient challenges,encompassing data quality,inherent biases,and ethical dimensions.We contend that this thorough analysis will serve as an invaluable scholarly resource,encapsulating the current state-of-the-art in crowdsourced geospatial data,and offering strategic directions for future interdisciplinary research and applications across various sectors.
基金Thiswork was supported by the National Natural Science Foundation of China[grant number 41371381],[grant number 41431177]Natural Science Research Program of Jiangsu[grant number 14KJA170001]+4 种基金National Special Program on Basic Works for Science and Technology of China[grant number 2013FY110900]National Key Technology Innovation Project for Water Pollution Control and Remediation[grant number 2013ZX07103006]National Basic Research Program of China[grant number 2015CB954102]GuiZhou Welfare and Basic Geological Research Program of China[grant number 201423]China Scholarship Council[grant number 201504910358].
文摘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.
基金This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA23100100]National Natural Science Foundation of China[grant number 41771430],[grant number 41631177]China Scholarship Council[grant number 201804910732].
文摘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.
基金the National Natural Science Foundation of China[grant number 41631177]the Chinese Academy of Sciences Key Project[grant number ZDRW-ZS-2016-6-3].
文摘Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.
文摘The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created.However,challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist.The objective of this paper is to present a publically available Geospatial Search Engine(GSE)that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data.The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services.A procedure runs daily to update map server layers and metadata,and to eliminate servers that go offline.The GSE supports Web Map Services,ArcGIS services,and websites that have geospatial data for download.We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services.While enhancements to our GSE and to web crawler technology in general lie ahead,our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data.
基金supported by the National Key Research and Development Program of China[grant number 2016YFB0502204]Opening research fund of State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing[grant number(16)Key04]+1 种基金Opening fund of Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation(Guangxi Teachers Education University)[grant number 2015GXESPKF02]National Natural Science Foundation of China[grant number 41401524].
文摘There is a critical need to develop a means for fast,task-driven discovery of geospatial data found in geoportals.Existing geoportals,however,only provide metadata-based means for discovery,with little support for task-driven discovery,especially when considering spatial–temporal awareness.To address this gap,this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery(CBR-GDD)method and implementation that accesses geospatial data by tasks.The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness,thus providing solutions based on past tasks.The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals:ontology-enhanced knowledge base,similarity assessment model,and case retrieval nets.A set of experiments and case studies validate the CBR-GDD approach and application,and demonstrate its efficiency.
基金This work is supported by Project 2007AA120502,sponsored by NHTRDPCthe National High Technology Research and Development Program of China.
文摘In recent years,geographical information systems have been employed in a wide variety of application domains,and as a result many research efforts are being devoted to those upcoming problems.Geospatial data security,especially access control,has attracted increased research interests within the academic community.The tendency towards sharing and interoperability of geospatial data and applications makes it common to acquire and integrate geospatial data from multiple organisations to accomplish a complex task.Meanwhile,many organisations have the requirement for securing access to possessed sensitive or proprietary geospatial data.In this heterogeneous and distributed environment,consistent access control functionality is crucial to promote controlled accessibility.As an extension of general access control mechanisms in the IT domain,the mechanism for geospatial data access control has its own requirements and characteristics of granularity and geospatial logic.In this paper,we address several fundamental aspects concerning the design and implementation of an access control system for geospatial data,including the classification,requirements,authorisation models,storage structures and management approaches for authorisation rules,matching and decision-making algorithms between authorisation rules and access requests,and its policy enforcement mechanisms.This paper also presents a system framework for realising access control functionality for geospatial data,and explain access control procedures in detail.
文摘The development of technology and the demands of groups of interest for standardized digital geospatial information are increasing daily.The necessity for referred geospatial information,according to a Referencing Coordinating System and European Standards,through a national GIS(Geographic Information System)system,requires a decision making of national and institutional importance.ASIG(State Authority for Geospatial Information)is the institution that administrates,implements,and maintains the NSDI(National Spatial Data Infrastructure).It is calculated that 80%of decision-making by public or private institutions uses geospatial data with a well-organized structure that enables efficiency.Thus,standardization of geospatial data by topic is one of the main objectives of implementing the NSDI in Albania.This is a complex task for the standard and the harmonization of geospatial data,which can be a good opportunity for professional awareness.This study shows in detail the methodology for the creation and implementation of data specification for geospatial information in Albania on the theme:Geology,adoption of the technical specification of the INSPIRE directive as well as the importance of ASIG as an institution that builds and maintains NSDI in Albania.
文摘With the rapid growth of the Internet,the copyright protection problem occurs frequently,and unauthorized copying and distributing of geospatial data threaten the investments of data producers.Digital watermarking is a possible solution to solve this issue.However,watermarking causes modifications in the original data resulting in distortion and affects accuracy,which is very important to geospatial vector data.This article provides distortion assessment of watermarked geospatial data using wavelet-based invisible watermarking.Eight wavelets at different wavelet decomposition levels are used for accuracy evaluation with the help of error measures such as maximum error and mean square error.Normalized correlation is used as a similarity index between original and extracted watermark.It is observed that the increase in the strength of embedding increases visual degradation.Haar wavelet outperforms the other wavelets,and the third wavelet decomposition level is proved to be optimal level for watermarking.
基金supported by Hexagon AB,a global provider of information technologies for geospatial and industrial enterprises.
文摘For centuries,humans’capacity to capture and depict physical space has played a central role in industrial and societal development.However,the digital revolution and the emergence of networked devices and services accelerate geospatial capture,coordination,and intelligence in unprecedented ways.Underlying the digital transformation of industry and society is the fusion of the physical and digital worlds-‘perceptality’-where geospatial perception and reality merge.This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application.Applications of sensors,robotics,cameras,machine learning,encryption,cloud computing and other software,and hardware intelligence are converging,enabling new ways for organizations and their equipment to perceive and capture reality.Meanwhile,demands for performance,reliability,and security are pushing compute‘to the edge’where real-time processing and coordination are vital.Big data place new restraints on economics,as pressures abound to actually use these data,both in real-time and for longer term strategic analysis and decision-making.These challenges require orchestration between information technology(IT)and operational technology(OT)and synchronization of diverse systems,data-sets,devices,environments,workflows,and people.
文摘The National Bureau of Surveying and Mapping of China has planned to speed up the development of spatial data infrastructure (SDI) in the coming few years.This SDI consists of four types of digital products,i.e.,digital orthophotos,digital elevation models, digital line graphs and digital raster graphs.For the DEM,a scheme for the database building and updating of 1∶10 000 digital elevation models has been proposed and some experimental tests have also been accomplished.This paper describes the theoretical (and/or technical) background and reports some of the experimental results to support the scheme.Various aspects of the scheme such as accuracy,data sources,data sampling,spatial resolution,terrain modeling,data organization,etc are discussed.
文摘The Niger Delta region of Nigeria is heavily impacted by petroleum explora-tion,refining activities,and industrial emissions,contributing to widespread aerial petro-pollutant contamination.This study investigates the hydrochemi-cal dispersion and geospatial correlation of atmospheric petroleum hydrocar-bons(TPH)deposited through rainfall within selected locations in Rivers State,Nigeria.Thirty-four rainwater samples were collected from seven distinct sub-regional study locations:Obigbo,Komkom,Obiama,Okoloma,Egberu,Umu Agbai,and Obete.Hydrochemical analyses were conducted using gas chroma-tography-flame ionization detection(GC-FID)to quantify total petroleum hy-drocarbons(TPH),with detailed compositional profiling of aliphatic hydro-carbons(C8-C40)and polycyclic aromatic hydrocarbons(PAHs).The disper-sion analysis employed Hexbin density mapping,Contour visualization,and spatial interpolation techniques to delineate pollution hotspots,revealing sig-nificant contamination gradients across the study region.The correlation ma-trix assessed interrelationships between hydrocarbon fractions and geographic positioning,identifying strong positive correlations(r>0.9)between TPH and total aliphatic hydrocarbons(TAH),suggesting transportation and industrial emissions as primary sources.PAHs exhibited localized concentration spikes,particularly near gas flaring zones and commercial hubs,implicating fossil fuel combustion,industrial activities,and long-range pollutant transport as domi-nant contamination mechanisms.Geospatial analysis indicates higher hydro-carbon deposition in the Western and Northern regions,with Obigbo and Okoloma experiencing the most significant contamination.The study high-lights rainfall as a key vector for atmospheric petrochemical deposition,with implications for water quality,ecosystem health,and human exposure risks.These findings emphasize the need for stricter environmental monitoring,reg-ulatory enforcement of industrial emissions,and strategic efforts to mitigate hydrocarbon pollution in petroleum-producing regions.
文摘坐标转换是空间地理数据应用的常用功能,由于部门及应用场景不同,不同坐标系成果的相互转换工作将长期存在。目前的坐标转换不仅需要脱离ArcGIS和AutoCAD等软件,还要实现主流矢量数据和栅格数据的坐标转换。为此提出了基于GDAL(geospatial data abstraction library)和Teigha库底层开发的通用坐标转换方法,给出了系统架构,实现了参数管理、参数计算、坐标转换、数据读写等功能。该方法大大提高了转换效率和保密性。实践表明,该系统转换结果正确可靠。
