ArcSDE作为空间数据引擎,在地理信息系统中有着广泛的应用,它能有效的提高空间数据库的管理效率。本文在对常用空间数据引擎比较的基础上,根据信息基础设施空间数据种类多、数据量大的特点,提出了基于ArcSDE+SQL Server 2000技术构建的...ArcSDE作为空间数据引擎,在地理信息系统中有着广泛的应用,它能有效的提高空间数据库的管理效率。本文在对常用空间数据引擎比较的基础上,根据信息基础设施空间数据种类多、数据量大的特点,提出了基于ArcSDE+SQL Server 2000技术构建的信息基础设施空间数据库建设方案,实现了海量数据的空间存储和有效管理,有利于信息的共享和充分利用,并在实际应用中取得了很好的效果。展开更多
Transport infrastructure plays an important role in shaping the configuration of spatial socio-economic structures and influencing regional accessibility. Although China＇s transport infrastructure has been experienci...Transport infrastructure plays an important role in shaping the configuration of spatial socio-economic structures and influencing regional accessibility. Although China＇s transport infrastructure has been experiencing a rapid development in the last 100 years, there lacks a systematic examination of the complete evolution history of China＇s transport development, particularly with all kinds of transport modes. This paper first aims to clarify the history of China＇s transportation from 1910 to 2012, and divides its evolution process into five periods （1911, 1935, 1953, 1981 and 2012） whereby each period represents the preliminary development time for each transport mode. Second, the paper calculates the transport dominance and analyses its spatial distribution in each period, with county as the basic analysis unit. Transport dominance here is defined as an integrated indicator for evaluating regional transport conditions. The results demonstrate the following： （1） areas with relative good transport dominance have expanded from scattered dots such as Tianjin, Shanghai, Guangzhou in 1911 to extensive areas with each provincial city as cores in 2012; （2） transport development is improved by advances in transportation technology. The construction of modern transport infrastructures such as seaports, airports, high-speed rails （HSRs）, and freeways lead the expansion of national territorial areas with good and excellent transport dominance and the increase of the value of transport dominance over time; （3） transport dominance is spatially unevenly distributed and exhibits resemblance with the expansion of transport network, which is closely related to China＇s socio-economic development and policles.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
Many visions for geospatial technology have been advanced over the past half century.Initially researchers saw the handling of geospatial data as the major problem to be overcome.The vision of geographic information s...Many visions for geospatial technology have been advanced over the past half century.Initially researchers saw the handling of geospatial data as the major problem to be overcome.The vision of geographic information systems arose as an early international consensus.Later visions included spatial data infrastructure,Digital Earth,and a nervous system for the planet.With accelerating advances in information technology,a new vision is needed that reflects today’s focus on open and multimodal access,sharing,engagement,the Web,Big Data,artificial intelligence,and data science.We elaborate on the concept of geospatial infrastructure,and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.展开更多
The utilization of urban underground space(UUS)offers an effective solution to urban problems but may also negatively affect urban development.Therefore,UUS development needs better concerted guidelines to coordinate ...The utilization of urban underground space(UUS)offers an effective solution to urban problems but may also negatively affect urban development.Therefore,UUS development needs better concerted guidelines to coordinate various urban systems and the multiple components of the underground world.Sustainable Development Goals(SDGs),which should be viewed as important yardsticks for UUS development,do not explicitly mention urban underground space,although many of them are affected by both the positive and negative consequences of its development.To fill this gap,this review lays the foundations of relevant UUS concepts and uses exemplary cases to reveal that 11 out of 17 SDGs can be linked with UUS uses.These linkages also manifest that land administration,integrated planning,architectural design,and construction technology are critical dimensions for increasing the contributions of UUS to the realization of SDGs.To achieve multi-disciplinary synergies among these four critical dimensions,a collaborative approach framework based on spatial data infrastructure is required.Thus,this work provides academics and practitioners with a holistic view of sustainable UUS development.展开更多
Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Mac...Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.展开更多
文摘ArcSDE作为空间数据引擎,在地理信息系统中有着广泛的应用,它能有效的提高空间数据库的管理效率。本文在对常用空间数据引擎比较的基础上,根据信息基础设施空间数据种类多、数据量大的特点,提出了基于ArcSDE+SQL Server 2000技术构建的信息基础设施空间数据库建设方案,实现了海量数据的空间存储和有效管理,有利于信息的共享和充分利用,并在实际应用中取得了很好的效果。
基金The Programme of Bingwei Excellent Young Scientists of the Institute of Geographic Sciences and Natural Resources Research, CAS, No.2011RC201 National Natural Science Foundation of China, No.41371143 No.41171107 Acknowledgement All the authors gratefully thank the reviewers and editor for their insightful and constructive comments. We especially thank Xi Hu at Environmental Change Institute, University of Oxford in the United Kingdom for editing the manuscript.
文摘Transport infrastructure plays an important role in shaping the configuration of spatial socio-economic structures and influencing regional accessibility. Although China＇s transport infrastructure has been experiencing a rapid development in the last 100 years, there lacks a systematic examination of the complete evolution history of China＇s transport development, particularly with all kinds of transport modes. This paper first aims to clarify the history of China＇s transportation from 1910 to 2012, and divides its evolution process into five periods （1911, 1935, 1953, 1981 and 2012） whereby each period represents the preliminary development time for each transport mode. Second, the paper calculates the transport dominance and analyses its spatial distribution in each period, with county as the basic analysis unit. Transport dominance here is defined as an integrated indicator for evaluating regional transport conditions. The results demonstrate the following： （1） areas with relative good transport dominance have expanded from scattered dots such as Tianjin, Shanghai, Guangzhou in 1911 to extensive areas with each provincial city as cores in 2012; （2） transport development is improved by advances in transportation technology. The construction of modern transport infrastructures such as seaports, airports, high-speed rails （HSRs）, and freeways lead the expansion of national territorial areas with good and excellent transport dominance and the increase of the value of transport dominance over time; （3） transport dominance is spatially unevenly distributed and exhibits resemblance with the expansion of transport network, which is closely related to China＇s socio-economic development and policles.
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.
文摘Many visions for geospatial technology have been advanced over the past half century.Initially researchers saw the handling of geospatial data as the major problem to be overcome.The vision of geographic information systems arose as an early international consensus.Later visions included spatial data infrastructure,Digital Earth,and a nervous system for the planet.With accelerating advances in information technology,a new vision is needed that reflects today’s focus on open and multimodal access,sharing,engagement,the Web,Big Data,artificial intelligence,and data science.We elaborate on the concept of geospatial infrastructure,and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.
基金This work was supported by the National Key Technology R&D Program(No.2012BAJ01B04)the National Natural Science Foundation of China(Grant No.42071251)the China Scholarship Council(File No.201806260167)。
文摘The utilization of urban underground space(UUS)offers an effective solution to urban problems but may also negatively affect urban development.Therefore,UUS development needs better concerted guidelines to coordinate various urban systems and the multiple components of the underground world.Sustainable Development Goals(SDGs),which should be viewed as important yardsticks for UUS development,do not explicitly mention urban underground space,although many of them are affected by both the positive and negative consequences of its development.To fill this gap,this review lays the foundations of relevant UUS concepts and uses exemplary cases to reveal that 11 out of 17 SDGs can be linked with UUS uses.These linkages also manifest that land administration,integrated planning,architectural design,and construction technology are critical dimensions for increasing the contributions of UUS to the realization of SDGs.To achieve multi-disciplinary synergies among these four critical dimensions,a collaborative approach framework based on spatial data infrastructure is required.Thus,this work provides academics and practitioners with a holistic view of sustainable UUS development.
基金supported by the Ministry of Education,Culture,Research and Technology,The Republic of Indonesia,and Institut Teknologi Sepuluh Nopember.
文摘Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.
