Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.Thi...Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.展开更多
Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information ...Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information and providing navigation services.展开更多
This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate ...This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.展开更多
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
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their...Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.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
Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigatio...Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigational efficiency.With the popularization of the Automatic Identification System(AIS),numerous studies utilized ship trajectories to identify maritime traffic patterns.However,the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior,such as ship static and maritime geospatial features,resulting in insufficient precision in ship traffic pattern recognition.This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity(STPMTS),which considers ship static feature,dynamic feature,port geospatial feature,as well as semantic relationships between these features.First,A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements.Subsequently,to quantify the degree of similarity of ship trajectories,a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features.Furthermore,trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters.The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns.Finally,the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China,ranging from September to November 2016.Compared with other methods,the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features,dynamic features,and adherence to navigation rules.This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.展开更多
During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed servi...During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed services.Compared to real-time similarity computations,direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency.However,missing and inconsistent service-registry information impedes accurate similarity relationship construction.Here,we propose a Graph Neural Network(GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags,which is named GSSR-GNN.As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability,a graph augmentation method for similarity relationship construction among second-order neighbors is proposed.Considering the differences in the semantic-information feature distributions,such as the service descriptions and tags,a feed-forward neural network-based fusion method is designed to embed them into the same vector space.Pre-trained Bidirectional Encoder Representations from Transformers(BERT)and WordNet models are introduced to enhance the service-representation expressiveness.When an enhanced service representation is input to the GNN,the similarity is calculated and the service similarity relationship is obtained.Experimental results show that the proposed model constructs service similarity relationships with high precision,thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.展开更多
Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity...Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.展开更多
Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can e...Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.展开更多
A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for buildi...A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure.In this paper,we propose the Geospatial Service Web(GSW)to underpin the development of a future geospatial cyberinfrastructure.The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies.The development of the GSW focuses on the establishment of a platform where data,information,and knowledge can be shared and exchanged in an interoperable manner.Theoretically,we describe the conceptual framework and research challenges for GSW,and then introduce our recent research toward building a GSW.A research agenda for building a GSW is also presented in the paper.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
The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social,economic,and environmental dimensions of people and the planet over the ...The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social,economic,and environmental dimensions of people and the planet over the next 15 years.Achieving sustainable development presents all countries and the global policy community with a set of significant development challenges that are almost entirely geographic in nature.Many of the issues impacting sustainable development can be analyzed,modeled,and mapped within a geographic context,which in turn can provide the integrative framework necessary for global collaboration,consensus and evidence-based decision-making.However,and despite significant advances in geospatial information technologies,there is a lack of awareness,understanding and uptake,particular at the policy and decision-making level,of the vital and integrative role of geospatial information and related enabling architectures such as National Spatial Data Infrastructures.This paper reasons that the role of geospatial information in contributing to sustainable development has not adequately been described by either the sustainable development policy practice or by the geospatial professional community.This lack of policy and guidance,with commensurate critical gaps and connection points with national geospatial frameworks,is a visible impediment to developing countries and those most affected by the challenges and need to achieve sustainable development.The global geospatial community now has a unique opportunity to integrate and connect geospatial information into the global development agenda in a more holistic and sustainable manner,specifically in contributing their data resources toward measuring and monitoring the 17 Sustainable Development Goals,and their 169 associated targets,through the global indicator framework that anchors the 2030 Agenda for Sustainable Development.This paper introduces and discusses a new strategic framework for linking a global policy to national geospatial capabilities.展开更多
Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full ...Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform： GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by： （1） extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; （2） unified 3D city models, support multiscale se- mantic representation of outdoor ＆ indoor and aboveground ＆ underground 3D objects; （3） high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and （4） high-performance visualization engine exploiting the massively parallel computation architecture of modem GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 knl2 of the whole Wuhan City, the largest city in middle China.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
Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimpli...Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.展开更多
This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover missions.Photogrammetric mapping techniques were used to produce topographic products ...This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover missions.Photogrammetric mapping techniques were used to produce topographic products of the landing site with meter level resolution using orbital images before landing,and to produce centimeter-resolution topographic products in near real-time after landing.Visual positioning techniques were used to determine the locations of the two landers using descent images and orbital basemaps immediately after landing.During surface operations,visual-positioning-based rover localization was performed routinely at each waypoint using Navcam images.The topographic analysis and rover localization results directly supported waypoint-to-waypoint path planning,science target selection and scientific investigations.A GIS-based digital cartography system was also developed to support rover teleoperation.展开更多
Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and ind...Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.展开更多
t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided ...t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.展开更多
基金supported by the NASA(Grant No.80NSSC21K0403)USAID Kansas State University subcontract KSU-A20-0163-S035 with Michigan State University.
文摘Agricultural drought,characterized by insufficient soil moisture crucial for crop growth,poses significant chal lenges to food security and economic sustainability,particularly in water-scarce regions like Senegal.This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System(RHEAS).This system,with a high-resolution of 0.05°,effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index(SMDI)-based agricultural drought monitoring.The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022.The SMDI,also provides a comprehensive understanding of regional variations in drought severity(S),duration(D),and frequency(F),through S-D-F analysis to identify key drought hotspots across Senegal.Findings reveal a distinct north-south gradient in drought conditions,with the northern and central Senegal experiencing more frequent and severe droughts.The study highlights that Senegal experiences frequent short-duration droughts with high severity,resulting in extensive spatial impact.Addition ally,increasing trends in drought severity and duration suggest evolving climate change effects.These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productiv ity.Specifically,the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice,as well as cash crops like peanuts.The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but also enables prioritizing sustainable approaches and adaptive strategies,ultimately sustaining agricultural productivity and resilience in Senegal’s drought-prone regions.
文摘Nowadays,spatiotemporal information,positioning,and navigation services have become critical components of new infrastructure.Precise positioning technology is indispensable for determining spatiotemporal information and providing navigation services.
文摘This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.
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.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
基金funded by Erasmus+ICM programme for a 3-month and 5-month stay at Lund University,Lund,Sweden,and thank the European Union.
文摘Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
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 National Natural Science Foundation of China[grant number 52371359]the Dalian Science and Technology Innovation Fund[grant number 2022JJ12GX015].
文摘Recognition of ship traffic patterns can provide insights into the rules of navigation,maneuvering,and collision avoidance for ships at sea.This is essential for ensuring safe navigation at sea and improving navigational efficiency.With the popularization of the Automatic Identification System(AIS),numerous studies utilized ship trajectories to identify maritime traffic patterns.However,the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior,such as ship static and maritime geospatial features,resulting in insufficient precision in ship traffic pattern recognition.This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity(STPMTS),which considers ship static feature,dynamic feature,port geospatial feature,as well as semantic relationships between these features.First,A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements.Subsequently,to quantify the degree of similarity of ship trajectories,a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features.Furthermore,trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters.The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns.Finally,the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China,ranging from September to November 2016.Compared with other methods,the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features,dynamic features,and adherence to navigation rules.This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.
基金supported by the National Natural Science Foundation of China[grant numbers41930107 and U20A2091].
文摘During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed services.Compared to real-time similarity computations,direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency.However,missing and inconsistent service-registry information impedes accurate similarity relationship construction.Here,we propose a Graph Neural Network(GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags,which is named GSSR-GNN.As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability,a graph augmentation method for similarity relationship construction among second-order neighbors is proposed.Considering the differences in the semantic-information feature distributions,such as the service descriptions and tags,a feed-forward neural network-based fusion method is designed to embed them into the same vector space.Pre-trained Bidirectional Encoder Representations from Transformers(BERT)and WordNet models are introduced to enhance the service-representation expressiveness.When an enhanced service representation is input to the GNN,the similarity is calculated and the service similarity relationship is obtained.Experimental results show that the proposed model constructs service similarity relationships with high precision,thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.
文摘Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.
文摘Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems.
基金This work is jointly supported by National Basic Research Program of China(Nos.2012CB719906 and 2011CB707105)National Natural Science Foundation of China(Nos.41023001,40801153 and 40901190).
文摘A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure.In this paper,we propose the Geospatial Service Web(GSW)to underpin the development of a future geospatial cyberinfrastructure.The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies.The development of the GSW focuses on the establishment of a platform where data,information,and knowledge can be shared and exchanged in an interoperable manner.Theoretically,we describe the conceptual framework and research challenges for GSW,and then introduce our recent research toward building a GSW.A research agenda for building a GSW is also presented in the paper.
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.
文摘The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social,economic,and environmental dimensions of people and the planet over the next 15 years.Achieving sustainable development presents all countries and the global policy community with a set of significant development challenges that are almost entirely geographic in nature.Many of the issues impacting sustainable development can be analyzed,modeled,and mapped within a geographic context,which in turn can provide the integrative framework necessary for global collaboration,consensus and evidence-based decision-making.However,and despite significant advances in geospatial information technologies,there is a lack of awareness,understanding and uptake,particular at the policy and decision-making level,of the vital and integrative role of geospatial information and related enabling architectures such as National Spatial Data Infrastructures.This paper reasons that the role of geospatial information in contributing to sustainable development has not adequately been described by either the sustainable development policy practice or by the geospatial professional community.This lack of policy and guidance,with commensurate critical gaps and connection points with national geospatial frameworks,is a visible impediment to developing countries and those most affected by the challenges and need to achieve sustainable development.The global geospatial community now has a unique opportunity to integrate and connect geospatial information into the global development agenda in a more holistic and sustainable manner,specifically in contributing their data resources toward measuring and monitoring the 17 Sustainable Development Goals,and their 169 associated targets,through the global indicator framework that anchors the 2030 Agenda for Sustainable Development.This paper introduces and discusses a new strategic framework for linking a global policy to national geospatial capabilities.
基金the National High Technology Research and Development Program of China (863 Program) (No. 2008AA121600)the National BasicResearch Program of China (973 Program)(No. 2010CB731801)the National Natural Science Foundation of China (No. 40871212)
文摘Aiming at the integrative management and comprehensive applications of large-scale 3D geospatial information covering the full 3D space of a city, this paper briefly introduces the design and implementation of a full 3D GIS platform： GeoScope, which provides a professional solution for the massive full three-dimensional geospatial data integration, management, analysis, visualization, and applications. GeoScope is characterized by： （1） extendible software architecture based on the hierarchical message bus, facilitates multimodal integrative applications of 2D GIS and 3D GIS; （2） unified 3D city models, support multiscale se- mantic representation of outdoor ＆ indoor and aboveground ＆ underground 3D objects; （3） high-efficient 3D geospatial database engine, supports integrated management of massive 3D geospatial data for real-time applications; and （4） high-performance visualization engine exploiting the massively parallel computation architecture of modem GPUs, supports real-time realistic rendering of large-scale complicated 3D geospatial environments. The successful pilot application of GeoScope is also illustrated with the 3D city models of 8494 knl2 of the whole Wuhan City, the largest city in middle China.
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Although big data are widely used in various fields,its application is still rare in the study of mining subsidence prediction(MSP)caused by underground mining.Traditional research in MSP has the problem of oversimplifying geological mining conditions,ignoring the fluctuation of rock layers with space.In the context of geospatial big data,a data-intensive FLAC3D(Fast Lagrangian Analysis of a Continua in 3 Dimensions)model is proposed in this paper based on borehole logs.In the modeling process,we developed a method to handle geospatial big data and were able to make full use of borehole logs.The effectiveness of the proposed method was verified by comparing the results of the traditional method,proposed method,and field observation.The findings show that the proposed method has obvious advantages over the traditional prediction results.The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7%and the standard deviation of the prediction results(which was 70 points)decreased by 39.4%,on average.The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.
基金This work was supported by the National Natural Science Foundation of China[grant number 41671458,41590851,41941003,and 41771488].
文摘This paper presents a brief overview of the geospatial technologies developed and applied in Chang’e-3 and Chang’e-4 lunar rover missions.Photogrammetric mapping techniques were used to produce topographic products of the landing site with meter level resolution using orbital images before landing,and to produce centimeter-resolution topographic products in near real-time after landing.Visual positioning techniques were used to determine the locations of the two landers using descent images and orbital basemaps immediately after landing.During surface operations,visual-positioning-based rover localization was performed routinely at each waypoint using Navcam images.The topographic analysis and rover localization results directly supported waypoint-to-waypoint path planning,science target selection and scientific investigations.A GIS-based digital cartography system was also developed to support rover teleoperation.
基金fully funded by the Center for Advanced Modeling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydneysupported by Researchers Supporting Project number RSP-2020/14,King Saud University,Riyadh,Saudi Arabia。
文摘Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation purposes.Earthquake prediction attracts the researchers'attention from both academia and industries.Traditionally,the risk assessment approaches have used various traditional and machine learning models.However,deep learning techniques have been rarely tested for earthquake probability mapping.Therefore,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE India.Then conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators.Prediction classification results and intensity variation were then used for probability and hazard mapping,respectively.Finally,earthquake risk map was produced by multiplying hazard,vulnerability,and coping capacity.The vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster management.The CNN model for a probability distribution is a robust technique that provides good accuracy.Results show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of 0.91.These indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.
文摘t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.
