Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance.To solve the task scheduling problem for remote sensing data processing in cloud computing,this paper...Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance.To solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem.Then,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method.Finally,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from experience.The experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing data.The results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.展开更多
Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense...Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.展开更多
This paper explores the use of cloud computing for remote sensing image processing.The main contribution of our work is to develop a remote sensing image processing platform based on cloud computing technology(OpenRS-...This paper explores the use of cloud computing for remote sensing image processing.The main contribution of our work is to develop a remote sensing image processing platform based on cloud computing technology(OpenRS-Cloud).This paper focuses on enabling methodical investigations into the development pattern,computational model,data management and service model exploring this novel distributed computing model.The experimental INSAR processing flow is implemented to verify the efficiency and feasibility of OpenRS-Cloud platform.The results show that cloud computing is well suited for computationally-intensive and data-intensive remote sensing services.展开更多
This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were proce...This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.展开更多
基金funded in part by the Key Research and Promotion Projects of Henan Province under Grant Nos.212102210079,222102210052,222102210007,and 222102210062.
文摘Task scheduling plays a crucial role in cloud computing and is a key factor determining cloud computing performance.To solve the task scheduling problem for remote sensing data processing in cloud computing,this paper proposes a workflow task scheduling algorithm—Workflow Task Scheduling Algorithm based on Deep Reinforcement Learning(WDRL).The remote sensing data process modeling is transformed into a directed acyclic graph scheduling problem.Then,the algorithm is designed by establishing a Markov decision model and adopting a fitness calculation method.Finally,combine the advantages of reinforcement learning and deep neural networks to minimize make-time for remote sensing data processes from experience.The experiment is based on the development of CloudSim and Python and compares the change of completion time in the process of remote sensing data.The results showthat compared with several traditionalmeta-heuristic scheduling algorithms,WDRL can effectively achieve the goal of optimizing task scheduling efficiency.
文摘Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.
基金supported by the National Natural Science Foundation of China(Grant No.40721001)the National Basic Research Program of China("973"Project)(Grant No.2006CB701304)the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2007AA120203)
文摘This paper explores the use of cloud computing for remote sensing image processing.The main contribution of our work is to develop a remote sensing image processing platform based on cloud computing technology(OpenRS-Cloud).This paper focuses on enabling methodical investigations into the development pattern,computational model,data management and service model exploring this novel distributed computing model.The experimental INSAR processing flow is implemented to verify the efficiency and feasibility of OpenRS-Cloud platform.The results show that cloud computing is well suited for computationally-intensive and data-intensive remote sensing services.
文摘This study examined wetland trends in the St.Lawrence Seaway(~500,000 km^(2))in Canada over the past four decades.To this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were processed.Reference samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged samples.These spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to 2021.Subsequently,a change analysis was conducted to calculate the loss and gain of different wetland types.Overall,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,respectively.It was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 years.Overall,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an increase.Finally,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),respectively.This study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.
