Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)...The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.展开更多
Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster...Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.展开更多
With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to th...With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.展开更多
With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data ...With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
Although big data is publicly available on water quality parameters,virtual simulation has not yet been adequately adapted in environmental chemistry research.Digital twin is different from conventional geospatial mod...Although big data is publicly available on water quality parameters,virtual simulation has not yet been adequately adapted in environmental chemistry research.Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic(e.g.,climate impact and water-related environmental catastrophe)or difficult to design and monitor in a real time(e.g.,pollutant and nutrient cycles in estuaries,soils,and sediments).Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios,including drinking water contamination.展开更多
Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extractio...Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre-and post-disaster RS images.However,RS images are usually limited to the revisit cycle and cloud cover.To solve this issue,social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster,which leads itself a compensation for RS images.In this paper,we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data,near real-time RS image and historical geographic information,in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability.Firstly,to generate a coarse waterlogging probability map,the historical inundated areas are derived from Digital Elevation Model(DEM)and historical waterlogging points,then the geographic features are extracted from DEM and RS image,which will be input to a Random Forest(RF)classifier to estimate the likelihood of hazards.Secondly,the real-time waterlogging-related information is extracted from social media data,where the Convolutional Neural Network(CNN)model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel.Finally,fine waterlogging probability map scan be generated based on morphological method,in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect.The 2016 Wuhan waterlogging and 2018 Chengdu water-logging are taken as case studies to demonstrate the effectiveness of the proposed framework.It can be concluded from the results that by integrating RS image and social media data,more accurate waterlogging probability maps can be generated,which can be further applied for inundated areas identification and disaster monitoring.展开更多
Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Theref...Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.展开更多
As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependabil...As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependability of predictive accuracy remains a critical issue,as the precision of these predictions largely hinges on the quality of the base data.We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions.Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data.Within the same season,we found that the relationship between the abundance of S.japonicus and environmental factors varied significantly depending on the data source.Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors.Additionally,in terms of model predictive performance,models based on field survey data demonstrated greater accuracy in predicting the abundance of S.japonicus compared to those based on remote sensing data,allowing for more accurate mastery of their spatial distribution characteristics.This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.展开更多
Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The ...Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.展开更多
Compressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we sho...Compressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we show the usage in groundwater mapping. Due to scarcity of water in many regions of the world, including southwestern United States, monitoring and management of groundwater is of utmost importance. A complete mapping of groundwater is difficult since the monitored sites are far from one another, and thus the data sets are considered extremely “sparse”. To overcome this difficulty in complete mapping of groundwater, compressive sensing is an ideal tool, as it bypasses the classical Nyquist criterion. We show that compressive sensing can effectively be used for reconstructions of groundwater level maps, by validating against data. This approach can have an impact on geographical sensing and information, as effective monitoring and management are enabled without constructing numerous or expensive measurement sites for groundwater.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
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
咸宁市是长江经济带的重要节点城市,科学编制自然资源资产负债表是实现区域可持续发展的重要保障。以咸宁市为研究区,探索基于遥感(Remote Sensing,RS)和地理信息系统(Geographic Information System,GIS)的自然资源资产负债表编制方法...咸宁市是长江经济带的重要节点城市,科学编制自然资源资产负债表是实现区域可持续发展的重要保障。以咸宁市为研究区,探索基于遥感(Remote Sensing,RS)和地理信息系统(Geographic Information System,GIS)的自然资源资产负债表编制方法。其间运用RS获取土地、矿产、森林等自然资源的空间分布与动态变化数据,结合GIS进行数据整合、分析及可视化表达,构建适用于区域尺度的自然资源资产负债表编制体系。研究成果明确了资产核算范围与负债计量标准,形成了“数据获取—处理分析—报表生成”的技术流程,为咸宁市自然资源管理与可持续发展决策提供了量化依据,也为同类地区的自然资源资产负债表编制提供了可借鉴的技术范式。展开更多
Using the multi-temporal Landsat data and survey data of national resources, the authors studied the dynamics of cultivated land and landcover changes of typical ecological regions in China. The results of investigati...Using the multi-temporal Landsat data and survey data of national resources, the authors studied the dynamics of cultivated land and landcover changes of typical ecological regions in China. The results of investigation showed that the whole distribution of the cultivated land shifted to Northeast and Northwest China, and as a result, the ecological quality of cultivated land dropped down. The seacoast and cultivated land in the area of Yellow River Mouth expanded by an increasing rate of 0.73 kma-1, with a depositing rate of 2.1 kma-1. The desertification area of the dynamic of Horqin Sandy Land increased from 60.02% of the total land area in1970s to 64.82% in1980s but decreased to 54.90% in early 1990s. As to the change of North Tibet lakes, the water area of the Namu Lake decreased by 38.58 km2 from year 1970 to 1988, with a decreasing rate of 2.14 km2a-1.展开更多
China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this pap...China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.展开更多
Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and a...Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.展开更多
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金The Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42407250)the Fund from Research Centre for Resources Engineering towards Carbon Neutrality(RCRE)of The Hong Kong Polytechnic University(Grant No.No.1-BBEM)the Fund from Natural Science Foundation of Jiangsu Province(Grant No.BK20241211)。
文摘The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
基金supported by the National Key Research and Development Program of China(2020YFC1512304).
文摘Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.
基金National Natural Science Foundation of China(Nos.42371406,42071441,42222106,61976234).
文摘With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.[42275147].
文摘With the development of remote sensing technology and computing science,remote sensing data present typical big data characteristics.The rapid development of remote sensing big data has brought a large number of data processing tasks,which bring huge challenges to computing.Distributed computing is the primary means to process remote sensing big data,and task scheduling plays a key role in this process.This study analyzes the characteristics of batch processing of remote sensing big data.This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow,called optimal sequence dynamic assignment algorithm,which is applicable to heterogeneously distributed computing environments.This strategy has two core contents:the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism.Moreover,the strategy solves the dependency,mismatch,and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks.The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm.We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy.Compared with the processing before optimization,the makespan of the proposed method was shortened by at least 20%.Compared with popular scheduling algorithm,the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
文摘Although big data is publicly available on water quality parameters,virtual simulation has not yet been adequately adapted in environmental chemistry research.Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic(e.g.,climate impact and water-related environmental catastrophe)or difficult to design and monitor in a real time(e.g.,pollutant and nutrient cycles in estuaries,soils,and sediments).Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios,including drinking water contamination.
基金This project was supported by the China Postdoctoral Science Foundation[grant number 2017M622522].
文摘Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre-and post-disaster RS images.However,RS images are usually limited to the revisit cycle and cloud cover.To solve this issue,social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster,which leads itself a compensation for RS images.In this paper,we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data,near real-time RS image and historical geographic information,in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability.Firstly,to generate a coarse waterlogging probability map,the historical inundated areas are derived from Digital Elevation Model(DEM)and historical waterlogging points,then the geographic features are extracted from DEM and RS image,which will be input to a Random Forest(RF)classifier to estimate the likelihood of hazards.Secondly,the real-time waterlogging-related information is extracted from social media data,where the Convolutional Neural Network(CNN)model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel.Finally,fine waterlogging probability map scan be generated based on morphological method,in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect.The 2016 Wuhan waterlogging and 2018 Chengdu water-logging are taken as case studies to demonstrate the effectiveness of the proposed framework.It can be concluded from the results that by integrating RS image and social media data,more accurate waterlogging probability maps can be generated,which can be further applied for inundated areas identification and disaster monitoring.
基金funded by China Geological Survey (grant no.1212011120899)the Department of Geology & Mining, China National Nuclear Corporation (grant no.201498)
文摘Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.
基金The Research Project of China Yangtze River Three Gorges Group Limited under contract No.201903173the Zhejiang Mariculture Research Institute of China under contract No.325000。
文摘As our understanding of ecology deepens and modeling techniques advance,species distribution models have grown increasingly sophisticated,enhancing both their fitting and predictive capabilities.However,the dependability of predictive accuracy remains a critical issue,as the precision of these predictions largely hinges on the quality of the base data.We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions.Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data.Within the same season,we found that the relationship between the abundance of S.japonicus and environmental factors varied significantly depending on the data source.Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors.Additionally,in terms of model predictive performance,models based on field survey data demonstrated greater accuracy in predicting the abundance of S.japonicus compared to those based on remote sensing data,allowing for more accurate mastery of their spatial distribution characteristics.This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.
基金supported by the National Natural Science Foundation of China[grant numbers 41971423 and 31972951]the Natural Science Foundation of Hunan Province[grant numbers 2020JJ3020 and 2020JJ5164]+1 种基金the Science and Technology Planning Project of Hunan Province[grant numbers 2019RS2043 and 2019GK2132]the Postgraduate Scientific Research Innovation Project of Hunan Province[grant number CX20210991].
文摘Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.
文摘Compressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we show the usage in groundwater mapping. Due to scarcity of water in many regions of the world, including southwestern United States, monitoring and management of groundwater is of utmost importance. A complete mapping of groundwater is difficult since the monitored sites are far from one another, and thus the data sets are considered extremely “sparse”. To overcome this difficulty in complete mapping of groundwater, compressive sensing is an ideal tool, as it bypasses the classical Nyquist criterion. We show that compressive sensing can effectively be used for reconstructions of groundwater level maps, by validating against data. This approach can have an impact on geographical sensing and information, as effective monitoring and management are enabled without constructing numerous or expensive measurement sites for groundwater.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
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.
文摘咸宁市是长江经济带的重要节点城市,科学编制自然资源资产负债表是实现区域可持续发展的重要保障。以咸宁市为研究区,探索基于遥感(Remote Sensing,RS)和地理信息系统(Geographic Information System,GIS)的自然资源资产负债表编制方法。其间运用RS获取土地、矿产、森林等自然资源的空间分布与动态变化数据,结合GIS进行数据整合、分析及可视化表达,构建适用于区域尺度的自然资源资产负债表编制体系。研究成果明确了资产核算范围与负债计量标准,形成了“数据获取—处理分析—报表生成”的技术流程,为咸宁市自然资源管理与可持续发展决策提供了量化依据,也为同类地区的自然资源资产负债表编制提供了可借鉴的技术范式。
基金National Natural Sci-ence Foundation of China (Grant No. 39900084) and KZCX1-10-07.
文摘Using the multi-temporal Landsat data and survey data of national resources, the authors studied the dynamics of cultivated land and landcover changes of typical ecological regions in China. The results of investigation showed that the whole distribution of the cultivated land shifted to Northeast and Northwest China, and as a result, the ecological quality of cultivated land dropped down. The seacoast and cultivated land in the area of Yellow River Mouth expanded by an increasing rate of 0.73 kma-1, with a depositing rate of 2.1 kma-1. The desertification area of the dynamic of Horqin Sandy Land increased from 60.02% of the total land area in1970s to 64.82% in1980s but decreased to 54.90% in early 1990s. As to the change of North Tibet lakes, the water area of the Namu Lake decreased by 38.58 km2 from year 1970 to 1988, with a decreasing rate of 2.14 km2a-1.
文摘China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.
基金financially supported by the funding appropriated from USDA-ARS National Program 305 Crop Productionthe 948 Program of Ministry of Agriculture of China (2016-X38)
文摘Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.
