为了应对乌克兰持续不断的战争带来的严峻挑战,EOS Data Analytics推出了“收获希望”计划,该计划旨在关注席卷乌克兰农业部门的危机。这个综合网页设有一张交互式地图,展示了2021—2024年乌克兰主要作物的历史和预测产量。此外,该倡议...为了应对乌克兰持续不断的战争带来的严峻挑战,EOS Data Analytics推出了“收获希望”计划,该计划旨在关注席卷乌克兰农业部门的危机。这个综合网页设有一张交互式地图,展示了2021—2024年乌克兰主要作物的历史和预测产量。此外,该倡议还介绍了乌克兰农业的现状及其对全球粮食安全的影响。出于支持乌克兰农民的承诺,该公司将在2024年向他们免费提供EOSDA作物监测服务,作为“收获希望”计划的一部分。该平台将帮助农民克服逆境,并确保乌克兰农业部门的可持续未来。展开更多
The Dragon Program is a cooperation in Earth Observation(EO)between the European Space Agency(ESA)and the Ministry of Science and Technology(MOST)of China.The collaboration aims to promote the application of ESA,ESA T...The Dragon Program is a cooperation in Earth Observation(EO)between the European Space Agency(ESA)and the Ministry of Science and Technology(MOST)of China.The collaboration aims to promote the application of ESA,ESA Third Party Mission,Copernicus Sentinel and China EO data in scientific and application development,facilitates scientific exchanges between China and Europe scientists,and provides training for land,ocean,and atmospheric applications of remote sensing technology.Started in 2004,four phases,each lasting four years,have been successfully completed.The Dragon Program has provided a unique platform for the joint exploitation of EO data for science and application development.Furthermore,it has made remarkable achievements by bringing together top scientists,training young talents,and facilitated satellite data sharing between the Sino-European teams.展开更多
In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive p...In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive performance greatly depends on large-scale computing resources,especially data cube infrastructure.In this study,from a green computing perspective,we propose a lightweight data cube model based on the preaggregation concept,in which the frequency histogram of EO data is employed as a specific measure.The cube space was divided into lattice pyramids by the Google S2 grid system,and histogram statistics of the EO data were injected into in-memory cuboids.Therefore,exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes.We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation.The experiments showed that the proposed model is smaller,faster and consumes less energy than ArcGIS Pro and XCube,and facilitates green computing strategies involving a cube infrastructure.Due to the standalone mode,larger dataset will result in longer cube building time with indexing latency.The efficiency of the approach comes at the expense of accuracy,and the inherent uncertainties were examined in this paper.展开更多
文摘为了应对乌克兰持续不断的战争带来的严峻挑战,EOS Data Analytics推出了“收获希望”计划,该计划旨在关注席卷乌克兰农业部门的危机。这个综合网页设有一张交互式地图,展示了2021—2024年乌克兰主要作物的历史和预测产量。此外,该倡议还介绍了乌克兰农业的现状及其对全球粮食安全的影响。出于支持乌克兰农民的承诺,该公司将在2024年向他们免费提供EOSDA作物监测服务,作为“收获希望”计划的一部分。该平台将帮助农民克服逆境,并确保乌克兰农业部门的可持续未来。
文摘The Dragon Program is a cooperation in Earth Observation(EO)between the European Space Agency(ESA)and the Ministry of Science and Technology(MOST)of China.The collaboration aims to promote the application of ESA,ESA Third Party Mission,Copernicus Sentinel and China EO data in scientific and application development,facilitates scientific exchanges between China and Europe scientists,and provides training for land,ocean,and atmospheric applications of remote sensing technology.Started in 2004,four phases,each lasting four years,have been successfully completed.The Dragon Program has provided a unique platform for the joint exploitation of EO data for science and application development.Furthermore,it has made remarkable achievements by bringing together top scientists,training young talents,and facilitated satellite data sharing between the Sino-European teams.
基金supported by Key Laboratory of National Geographic Census and Monitoring,Ministry of Natural Resources,China:[Grant Number 2020NGCM05]Natural Science Foundation of Shaanxi Province,China:[Grant Number 2020JQ-413].
文摘In the era of Earth Observation(EO)big data,interactive spatiotemporal aggregation analysis is a critical tool for exploring geographic patterns.However,existing methods are inefficient and complex.Their interactive performance greatly depends on large-scale computing resources,especially data cube infrastructure.In this study,from a green computing perspective,we propose a lightweight data cube model based on the preaggregation concept,in which the frequency histogram of EO data is employed as a specific measure.The cube space was divided into lattice pyramids by the Google S2 grid system,and histogram statistics of the EO data were injected into in-memory cuboids.Therefore,exploratory aggregation analysis of EO datasets could be rapidly converted into multidimensional-view query processes.We implemented the prototype system on a local PC and conducted a case study of global vegetation index aggregation.The experiments showed that the proposed model is smaller,faster and consumes less energy than ArcGIS Pro and XCube,and facilitates green computing strategies involving a cube infrastructure.Due to the standalone mode,larger dataset will result in longer cube building time with indexing latency.The efficiency of the approach comes at the expense of accuracy,and the inherent uncertainties were examined in this paper.
