Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data,especially for managing satellite time series.These infrastructures build on the concept of multidimensional data m...Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data,especially for managing satellite time series.These infrastructures build on the concept of multidimensional data model(data hypercube)and are complex systems engaging different disciplines and expertise.For this reason,their interoperability capacity has become a challenge in the Global Change and Earth System science domains.To address this challenge,there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features.In this respect,a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain.This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas,with the final goal of enabling and facilitating interoperability.It introduces six modeling views,each of them is described according to:its main concerns,principal stakeholders,and possible patterns to be used.The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional“cubes”along with the more recent and analogous development in the Earth Observation domain,and puts forward a set of interoperability recommendations based on the modeling views.展开更多
Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data...Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data—is creating new opportunities for the Earth sciences and revolutionizing the innovation of methodologies and thought patterns. It has potential to advance in-depth development of Earth sciences and bring more exciting scientific discoveries.The Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space was held in Beijing in June of 2015.The forum analyzed the development of Earth observation technology and big data, explored the concepts and scientific connotations of Big Earth Data from space, discussed the correlation between Big Earth Data and Digital Earth, and dissected the potential of Big Earth Data from space to promote scientific discovery in the Earth sciences, especially concerning global changes.展开更多
Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic...Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science.展开更多
The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and moni...The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.展开更多
The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before.These changes will unlikely stop or eve...The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before.These changes will unlikely stop or even decelerate in the near future.There is an urgent need for a new scientific approach and an advanced form of evidence-based decisionmaking towards the benefit of society,the economy,and the environment.To understand the impacts and interrelationships between humans as a society and natural Earth system processes,we propose a new engineering discipline,Big Earth Data science.This science is called to provide the methodologies and tools to generate knowledge from diverse,numerous,and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth.Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves.The manuscript introduces the universe of discourse characterizing this new science,its foundational paradigms and methodologies,and a possible technological framework to be implemented by applying an ecosystem approach.CASEarth and GEOSS are presented as examples of international implementation attempts.Conclusions discuss important challenges and collaboration opportunities.展开更多
Digital Earth has seen great progress during the last 19 years.When it entered into the era of big data,Digital Earth developed into a new stage,namely one characterized by‘Big Earth Data’,confronting new challenges...Digital Earth has seen great progress during the last 19 years.When it entered into the era of big data,Digital Earth developed into a new stage,namely one characterized by‘Big Earth Data’,confronting new challenges and opportunities.In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development.Then,the opportunities and challenges that Big Earth Data faces are discussed.As a data-intensive scientific research approach,Big Earth Data provides a new vision and methodology to Earth sciences,and the paper identifies the advantages of Big Earth Data to scientific research,especially in knowledge discovery and global change research.We believe that Big Earth Data will advance and promote the development of Digital Earth.展开更多
A persistent challenge for the Sustainable Development Goals(SDGs)has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to con-duct these assessments.Rapid de...A persistent challenge for the Sustainable Development Goals(SDGs)has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to con-duct these assessments.Rapid developments in big data,however,are facilitating a global approach to the SDGs.Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indica-tors.Big Earth Data,a special class of big data,integrates multisource data within a geographic context,utilizing the principles and methodologies of the established literature on big data science,applied specifically to Earth system science.This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and mea-sure progress towards the SDGs.As an example,the Big Earth Data Science Engineering Program(CASEarth)of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs.Lastly,the paper proposes future priorities for developments in Big Earth Data,such as human resource capacity,digital infrastructure,interoperability,and envir-onmental considerations.展开更多
Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis wor...Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity.Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side.Four practical examples are presented from the marine,climate,planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow.Web service technologies offer a time-and cost-effective way to access multidimensional data in a user-tailored format and allow for rapid application development or time-series extraction.Data transport is minimised and enhanced processing capabilities are offered.More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces.At the same time,data users have to become aware of the advantages of web services and be trained how to benefit from them most.展开更多
Big Earth Data analysis is a complex task requiring the integration of many skills and technologies.This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space ...Big Earth Data analysis is a complex task requiring the integration of many skills and technologies.This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space and presents examples of state-of-the-art projects in each major branch of Big Earth Data research.Current issues within Big Earth Data research are highlighted and potential future solutions identified.展开更多
Cloud-based services introduce a paradigm shift in how users access,process and analyse Big Earth data.A key challenge is to align the current state of how users access,process and analyse the data with trends and roa...Cloud-based services introduce a paradigm shift in how users access,process and analyse Big Earth data.A key challenge is to align the current state of how users access,process and analyse the data with trends and roadmaps large data organisations layout.In addition,due to the increased availability of open data,a more diverse user base wants to take advantage of Earth science data leading to new user requirements.We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise.Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs.These gaps demand efforts on three levels.First,cloud services shall be targeted at intermediate users instead of policy-and decision-makers and over-engineered systems with a high level of abstraction should be avoided.Second,more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake.Third,a cloud certification mechanism could help in building up overall trust in cloud-based services.展开更多
Quantitative assessment of community resilience can provide support for hazard mitigation,disaster risk reduction,disaster relief,and long-term sustainable development.Traditional resilience assessment tools are mostl...Quantitative assessment of community resilience can provide support for hazard mitigation,disaster risk reduction,disaster relief,and long-term sustainable development.Traditional resilience assessment tools are mostly theory-driven and lack empirical validation,which impedes scientific understanding of community resilience and practical decision-making of resilience improvement.In the advent of the Big Data Era,the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand,measure,and promote community resilience.This article provides a comprehensive review of the definitions of community resilience,along with the traditional and emerging data and methods of quantitative resilience measurement.The theoretical bases,modeling principles,advantages,and disadvantages of the methods are discussed.Finally,we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience.This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.展开更多
Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change,sustainable development,and other issues of global concern.For example,analyzing massive amounts of sat...Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change,sustainable development,and other issues of global concern.For example,analyzing massive amounts of satellite imagery of polar environments,which are sensitive to the effects of climate change,provides insights into global climate trends.This study proposes a method to use Big Earth Data to explore changes in snowmelt over the Antarctic ice sheet from 1979 to 2016.The method uses Zernike moments to observe melt area in Antarctica and uses the Mann-Kendall test to detect temporal changes and abnormal information about the continent’s melt area.The melting trend in the time-series data matched the changes in temperature and seasonal transitions.The results do not demonstrate significant change in the area of surface melt;however,abrupt changes in melt conditions linked to temperature changes over the Antarctic ice sheet were observed within the time series.The experiment results demonstrate that the proposed method is robust,adaptive,and capable of extracting the core features of melting snow.展开更多
An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source ...An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.展开更多
Big Earth Data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and biology.An effective data classification system and label management s...Big Earth Data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and biology.An effective data classification system and label management strategy are important foundations for long-term management of data resources.The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program(CASEarth).This study constructed two sets of classification and coding systems that realize classification by mapping each other;namely,the geosphere-level and Sustainable Development Goals(SDGs)indicator classifications.This technique was based on natural language processing technology and solved problems with subject-word segmentation,weight calculation,and dynamic matching.A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100.Furthermore,we expect our study to provide the methodology and technical support for useroriented classification and label management services for Big Earth Data.展开更多
In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and ...In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.展开更多
Leaders are increasingly calling for improved decision support to manage human and environmental challenges in the 21^(st)Century.The 17 United Nations Sustainable Development Goals(SDGs)pro-vide a framing of these ch...Leaders are increasingly calling for improved decision support to manage human and environmental challenges in the 21^(st)Century.The 17 United Nations Sustainable Development Goals(SDGs)pro-vide a framing of these challenges,wherein 169 targets require significant data to be monitored and pursued effectively.However,many targets are still not connected with big Earth data capabilities.In this conceptual paper,the authors sought to answer the question“How are partnerships influencing progress in using big Earth data to address the SDGs?”Using the Pivotal Principles for Digital Earth,we reflect on the geospatial sector’s partnering efforts and opportunities for enhancing the use of big Earth data.We use Australia as a case study to explore partnering for action towards one or more SDGs.We conclude that partnerships are emerging for big Earth data use in addressing the SDGs,but much can still be done to harness the power of partnerships for transformative SDG outcomes.We propose four key enabling priorities:1)multiple-stakeholder collaboration,2)regular enactment of the problem-solving cycle,3)transparent and reliable georeferenced data,and 4)development and preservation of trust.Five“next steps”are outlined for Australia,which can also benefit practitioners and leaders globally in problem-solving for the SDGs.展开更多
In this paper,we present GSio,a software system for serving geospatial raster or gridded Big Earth Data at scale.GSio allows different scientific communities to consume geospatial analysis ready data.It provides a gen...In this paper,we present GSio,a software system for serving geospatial raster or gridded Big Earth Data at scale.GSio allows different scientific communities to consume geospatial analysis ready data.It provides a generic interface to the data,which removes the need to interact with individual files,and can interoperate with existing geospatial collections hosted on data centres and public clouds.A distributed compute model is used to read and transform the data in parallel using a cluster of compute nodes for delivering data as a service to users.Several use cases are presented demonstrating different scenarios where this service has been used.展开更多
Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and...Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.展开更多
The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observat...The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observation data and big human behavior data.A description of big geodata includes,in addition to the“5Vs”(volume,velocity,value,variety and veracity),a further five features,that is,granularity,scope,density,skewness and precision.Based on this approach,the essence of mining big geodata includes four aspects.First,flow space,where flow replaces points in traditional space,will become the new presentation form for big human behavior data.Second,the objectives for mining big geodata are the spatial patterns and the spatial relationships.Third,the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data,namely heterogeneity and homogeneity,may change with scale.Fourth,data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships.The big geodata mining methods may be categorized into two types in view of the mining objective,i.e.,classification mining and relationship mining.Future research will be faced by a number of issues,including the aggregation and connection of big geodata,the effective evaluation of the mining results and the challenge for mining to reveal“non-trivial”knowledge.展开更多
Paleogeographic analysis accounts for an essential part of geological research,making important contributions in the reconstruction of depositional environments and tectonic evolution histories(Ingalls et al.,2016;Mer...Paleogeographic analysis accounts for an essential part of geological research,making important contributions in the reconstruction of depositional environments and tectonic evolution histories(Ingalls et al.,2016;Merdith et al.,2017),the prediction of mineral resource distributions in continental sedimentary basins(Sun and Wang,2009),and the investigation of climate patterns and ecosystems(Cox,2016).展开更多
基金This research was supported by the European Commission in the framework of the H2020 ECOPOTENTIAL project(ID 641762)the H2020 SeaDataCloud project(ID 730960),and the FP7 EarthServer project(ID 283610).
文摘Big Earth Data-Cube infrastructures are becoming more and more popular to provide Analysis Ready Data,especially for managing satellite time series.These infrastructures build on the concept of multidimensional data model(data hypercube)and are complex systems engaging different disciplines and expertise.For this reason,their interoperability capacity has become a challenge in the Global Change and Earth System science domains.To address this challenge,there is a pressing need in the community to reach a widely agreed definition of Data-Cube infrastructures and their key features.In this respect,a discussion has started recently about the definition of the possible facets characterizing a Data-Cube in the Earth Observation domain.This manuscript contributes to such debate by introducing a view-based model of Earth Data-Cube systems to design its infrastructural architecture and content schemas,with the final goal of enabling and facilitating interoperability.It introduces six modeling views,each of them is described according to:its main concerns,principal stakeholders,and possible patterns to be used.The manuscript considers the Business Intelligence experience with Data Warehouse and multidimensional“cubes”along with the more recent and analogous development in the Earth Observation domain,and puts forward a set of interoperability recommendations based on the modeling views.
基金supported by the Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space
文摘Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data—is creating new opportunities for the Earth sciences and revolutionizing the innovation of methodologies and thought patterns. It has potential to advance in-depth development of Earth sciences and bring more exciting scientific discoveries.The Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space was held in Beijing in June of 2015.The forum analyzed the development of Earth observation technology and big data, explored the concepts and scientific connotations of Big Earth Data from space, discussed the correlation between Big Earth Data and Digital Earth, and dissected the potential of Big Earth Data from space to promote scientific discovery in the Earth sciences, especially concerning global changes.
基金This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Project title:CASEarth(XDA19000000)and Digital Belt and Road(XDA19030000).
文摘Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science.
基金supported by the Big Earth Data Science Engineering Program of the Chinese Academy of Sciences Strategic Priority Research Program(XDA19090000 and XDA19030000)。
文摘The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.
基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant numbers XDA19030000 and XDA19090000)the DG Research and Innovation of the European Commission(H2020 grant number 34538).
文摘The digital transformation of our society coupled with the increasing exploitation of natural resources makes sustainability challenges more complex and dynamic than ever before.These changes will unlikely stop or even decelerate in the near future.There is an urgent need for a new scientific approach and an advanced form of evidence-based decisionmaking towards the benefit of society,the economy,and the environment.To understand the impacts and interrelationships between humans as a society and natural Earth system processes,we propose a new engineering discipline,Big Earth Data science.This science is called to provide the methodologies and tools to generate knowledge from diverse,numerous,and complex data sources necessary to ensure a sustainable human society essential for the preservation of planet Earth.Big Earth Data science aims at utilizing data from Earth observation and social sensing and develop theories for understanding the mechanisms of how such a social-physical system operates and evolves.The manuscript introduces the universe of discourse characterizing this new science,its foundational paradigms and methodologies,and a possible technological framework to be implemented by applying an ecosystem approach.CASEarth and GEOSS are presented as examples of international implementation attempts.Conclusions discuss important challenges and collaboration opportunities.
文摘Digital Earth has seen great progress during the last 19 years.When it entered into the era of big data,Digital Earth developed into a new stage,namely one characterized by‘Big Earth Data’,confronting new challenges and opportunities.In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development.Then,the opportunities and challenges that Big Earth Data faces are discussed.As a data-intensive scientific research approach,Big Earth Data provides a new vision and methodology to Earth sciences,and the paper identifies the advantages of Big Earth Data to scientific research,especially in knowledge discovery and global change research.We believe that Big Earth Data will advance and promote the development of Digital Earth.
基金The research was supported by the Chinese Academy of Sciences Strategic Priority Research Program of the Big Earth Data Science Engineering Program(CASEarth),grant numbers[XDA19090000 and XDA19030000].
文摘A persistent challenge for the Sustainable Development Goals(SDGs)has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to con-duct these assessments.Rapid developments in big data,however,are facilitating a global approach to the SDGs.Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indica-tors.Big Earth Data,a special class of big data,integrates multisource data within a geographic context,utilizing the principles and methodologies of the established literature on big data science,applied specifically to Earth system science.This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and mea-sure progress towards the SDGs.As an example,the Big Earth Data Science Engineering Program(CASEarth)of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs.Lastly,the paper proposes future priorities for developments in Big Earth Data,such as human resource capacity,digital infrastructure,interoperability,and envir-onmental considerations.
基金the European Union’s Horizon 2020 Framework Programme research and innovation agreement[grant number 654367]。
文摘Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity.Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side.Four practical examples are presented from the marine,climate,planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow.Web service technologies offer a time-and cost-effective way to access multidimensional data in a user-tailored format and allow for rapid application development or time-series extraction.Data transport is minimised and enhanced processing capabilities are offered.More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces.At the same time,data users have to become aware of the advantages of web services and be trained how to benefit from them most.
文摘Big Earth Data analysis is a complex task requiring the integration of many skills and technologies.This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space and presents examples of state-of-the-art projects in each major branch of Big Earth Data research.Current issues within Big Earth Data research are highlighted and potential future solutions identified.
文摘Cloud-based services introduce a paradigm shift in how users access,process and analyse Big Earth data.A key challenge is to align the current state of how users access,process and analyse the data with trends and roadmaps large data organisations layout.In addition,due to the increased availability of open data,a more diverse user base wants to take advantage of Earth science data leading to new user requirements.We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise.Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs.These gaps demand efforts on three levels.First,cloud services shall be targeted at intermediate users instead of policy-and decision-makers and over-engineered systems with a high level of abstraction should be avoided.Second,more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake.Third,a cloud certification mechanism could help in building up overall trust in cloud-based services.
基金supported by the U.S.National Science Foundation under the Methodology,Measurement&Statistics(MMS)Program(Award#:2102019)the Human Networks&Data Science Infrastructure Program(Award#:2318204&2318206)+1 种基金the Smart and Connected Communities(Award#:2325631)Texas A&M University Innovation[X]Program.
文摘Quantitative assessment of community resilience can provide support for hazard mitigation,disaster risk reduction,disaster relief,and long-term sustainable development.Traditional resilience assessment tools are mostly theory-driven and lack empirical validation,which impedes scientific understanding of community resilience and practical decision-making of resilience improvement.In the advent of the Big Data Era,the increasing data availability and advances in computing and modeling techniques offer new opportunities to understand,measure,and promote community resilience.This article provides a comprehensive review of the definitions of community resilience,along with the traditional and emerging data and methods of quantitative resilience measurement.The theoretical bases,modeling principles,advantages,and disadvantages of the methods are discussed.Finally,we point out research avenues to overcome the existing challenges and develop robust methods to measure and promote community resilience.This article establishes guidance for scientists to further advance disaster research and for planners and policymakers to design actionable tools to develop sustainable and resilient communities.
基金supported by the Chinese Academy of Sciences Strategic Priority Research Program of the Big Earth Data Science Engineering Program(CASEarth)[grant numbers XDA19090000,XDA19030000]National Natural Science Foundation of China[grant number 41876226]。
文摘Big Earth Data—big data associated with Earth sciences—can potentially revolutionize research on climate change,sustainable development,and other issues of global concern.For example,analyzing massive amounts of satellite imagery of polar environments,which are sensitive to the effects of climate change,provides insights into global climate trends.This study proposes a method to use Big Earth Data to explore changes in snowmelt over the Antarctic ice sheet from 1979 to 2016.The method uses Zernike moments to observe melt area in Antarctica and uses the Mann-Kendall test to detect temporal changes and abnormal information about the continent’s melt area.The melting trend in the time-series data matched the changes in temperature and seasonal transitions.The results do not demonstrate significant change in the area of surface melt;however,abrupt changes in melt conditions linked to temperature changes over the Antarctic ice sheet were observed within the time series.The experiment results demonstrate that the proposed method is robust,adaptive,and capable of extracting the core features of melting snow.
基金the C-SCALE project(https://c-scale.eu/),which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017529。
文摘An adequate compute and storage infrastructure supporting the full exploitation of Copernicus and Earth Observation datasets is currently not available in Europe.This paper presents the cross-disciplinary open-source technologies being leveraged in the C-SCALE project to develop an open federation of compute and data providers as an alternative to monolithic infrastructures for processing and analysing Copernicus and Earth Observation data.Three critical aspects of the federation and the chosen technologies are elaborated upon:(1)federated data discovery,(2)federated access and(3)software distribution.With these technologies the open federation aims to provide homogenous access to resources,thereby enabling its users to generate meaningful results quickly and easily.This will be achieved by abstracting the complexity of infrastructure resource access provisioning and orchestration,including discovery of data across distributed archives,away from the end-users.Which is needed because end-users wish to focus on analysing ready-to-use data products and models rather than spending their time on the setup and maintenance of complex and heterogeneous IT infrastructures.The open federation will support processing and analysing the vast amounts of Copernicus and Earth Observation data that are critical for the implementation of the Destination Earth resp.Digital Twins vision for a high precision digital model of the Earth to model,monitor and simulate natural phenomena and related human activities.
基金the Big Earth Science Engineering Program(CASEarth)of the Chinese Academy of Sciences[XDA19090200 and XDA19040501].
文摘Big Earth Data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and biology.An effective data classification system and label management strategy are important foundations for long-term management of data resources.The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program(CASEarth).This study constructed two sets of classification and coding systems that realize classification by mapping each other;namely,the geosphere-level and Sustainable Development Goals(SDGs)indicator classifications.This technique was based on natural language processing technology and solved problems with subject-word segmentation,weight calculation,and dynamic matching.A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100.Furthermore,we expect our study to provide the methodology and technical support for useroriented classification and label management services for Big Earth Data.
文摘In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.
基金This work has not received any funding.This paper is constructed from literature reviews and insights from a Special Session on the SDGs,by the co-authors and invited panelists,at LOCATE21(Brisbane,30 March 2021).
文摘Leaders are increasingly calling for improved decision support to manage human and environmental challenges in the 21^(st)Century.The 17 United Nations Sustainable Development Goals(SDGs)pro-vide a framing of these challenges,wherein 169 targets require significant data to be monitored and pursued effectively.However,many targets are still not connected with big Earth data capabilities.In this conceptual paper,the authors sought to answer the question“How are partnerships influencing progress in using big Earth data to address the SDGs?”Using the Pivotal Principles for Digital Earth,we reflect on the geospatial sector’s partnering efforts and opportunities for enhancing the use of big Earth data.We use Australia as a case study to explore partnering for action towards one or more SDGs.We conclude that partnerships are emerging for big Earth data use in addressing the SDGs,but much can still be done to harness the power of partnerships for transformative SDG outcomes.We propose four key enabling priorities:1)multiple-stakeholder collaboration,2)regular enactment of the problem-solving cycle,3)transparent and reliable georeferenced data,and 4)development and preservation of trust.Five“next steps”are outlined for Australia,which can also benefit practitioners and leaders globally in problem-solving for the SDGs.
基金The authors wish to acknowledge funding from the Australian Government Department of Educationthrough the National Collaboration Research Infrastructure Strategy(NCRIS)and the EducationInvestment Fund(EIF)Super Science Initiatives through the National Computational Infrastructure(NCI)Research Data Storage Infrastructure(RDSI)and Research Data Services(RDS)Projects.
文摘In this paper,we present GSio,a software system for serving geospatial raster or gridded Big Earth Data at scale.GSio allows different scientific communities to consume geospatial analysis ready data.It provides a generic interface to the data,which removes the need to interact with individual files,and can interoperate with existing geospatial collections hosted on data centres and public clouds.A distributed compute model is used to read and transform the data in parallel using a cluster of compute nodes for delivering data as a service to users.Several use cases are presented demonstrating different scenarios where this service has been used.
基金funded by the International Cooperation and Exchanges National Natural Science Foundation of China (41120114001)
文摘Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.
基金National Natural Science Foundation of China,No.41525004,No.41421001。
文摘The objective,connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper.Big geodata may be categorized into two domains:big earth observation data and big human behavior data.A description of big geodata includes,in addition to the“5Vs”(volume,velocity,value,variety and veracity),a further five features,that is,granularity,scope,density,skewness and precision.Based on this approach,the essence of mining big geodata includes four aspects.First,flow space,where flow replaces points in traditional space,will become the new presentation form for big human behavior data.Second,the objectives for mining big geodata are the spatial patterns and the spatial relationships.Third,the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data,namely heterogeneity and homogeneity,may change with scale.Fourth,data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships.The big geodata mining methods may be categorized into two types in view of the mining objective,i.e.,classification mining and relationship mining.Future research will be faced by a number of issues,including the aggregation and connection of big geodata,the effective evaluation of the mining results and the challenge for mining to reveal“non-trivial”knowledge.
基金granted by the National Natural Science Foundation of China(Grant No.41802126)Open Fund of Key Laboratory of Sedimentary Mineralization and Sedimentary Minerals in Shandong Province(Grant No.DMSM2017006).
文摘Paleogeographic analysis accounts for an essential part of geological research,making important contributions in the reconstruction of depositional environments and tectonic evolution histories(Ingalls et al.,2016;Merdith et al.,2017),the prediction of mineral resource distributions in continental sedimentary basins(Sun and Wang,2009),and the investigation of climate patterns and ecosystems(Cox,2016).
