Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,...Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.展开更多
Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rel...Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.展开更多
Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abro...Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS,World Wind,Google Earth,Google Maps,Bing Maps,Microsoft TerraServer,ESA,Earth Simulator,GeoEye,Map World,China Centre for Resources Satellite Data and Application,National Satellite Meteorological Centre,and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture,it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.展开更多
The sharing of pathological data is highly important in various applications,such as remote diagnosis,graded diagnosis,illness treatment,and specialist system development.However,ensuring reliable,secure,privacy-prese...The sharing of pathological data is highly important in various applications,such as remote diagnosis,graded diagnosis,illness treatment,and specialist system development.However,ensuring reliable,secure,privacy-preserving,and efficient sharing of pathological data poses significant challenges.This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing.Additionally,it employs conditional proxy re-encryption(C-PRE)and public key encryption with equality test technology to control the scope and preserve the privacy of shared data.To assess the practicality of our solution,we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics.We also compared the solution with relevant schemes.The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.展开更多
基金provided by the Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121,973)DSI-NRF CIMERA.Yousef Ghorbani acknowledges financial support from the Centre for Advanced Mining and Metallurgy(CAMM),a strategic research environment established at the LuleåUniversity of Technology funded by the Swedish governmentWe also thank Sibanye-Stillwater Ltd.For their funding through the Wits Mining Institute(WMI).
文摘Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big data.Its spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral bands.The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data.The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are important.Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific reduction.Furthermore,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis.Currently,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences.In this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation.We use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing satellites.We show that various machine learning algorithms can be used given the abundance of training data.Consequently,we are able to produce a high resolution(10 m grid size)gold concentration map of the TSF,which demonstrates the potential of our method to be used to guide extraction planning,online resource exploration,environmental monitoring and resource estimation.
基金supported by a Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121973)and DSI-NRF CIMERA.
文摘Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.
基金supported by the National Basic Research Program of China ("973" Program) (Grant No.61399)
文摘Aiming at the storage and management problems of massive remote sensing data,this paper gives a comprehensive analysis of the characteristics and advantages of thirteen data storage centers or systems at home and abroad. They mainly include the NASA EOS,World Wind,Google Earth,Google Maps,Bing Maps,Microsoft TerraServer,ESA,Earth Simulator,GeoEye,Map World,China Centre for Resources Satellite Data and Application,National Satellite Meteorological Centre,and National Satellite Ocean Application Service. By summing up the practical data storage and management technologies in terms of remote sensing data storage organization and storage architecture,it will be helpful to seek more suitable techniques and methods for massive remote sensing data storage and management.
基金supported by National Natural Science Foundation of China under Grant 61972438Wuhu Science and Tech-nology Plan Project under Grant 2022yf50Key Research and Develop-ment Projects in Anhui Province under Grant 202004a05020002 and 2022a05020049.
文摘The sharing of pathological data is highly important in various applications,such as remote diagnosis,graded diagnosis,illness treatment,and specialist system development.However,ensuring reliable,secure,privacy-preserving,and efficient sharing of pathological data poses significant challenges.This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing.Additionally,it employs conditional proxy re-encryption(C-PRE)and public key encryption with equality test technology to control the scope and preserve the privacy of shared data.To assess the practicality of our solution,we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics.We also compared the solution with relevant schemes.The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.
