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
Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of ...Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems（Verburg et al.,2015）.展开更多
Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high resolution.We combined optical and SAR images,based...Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high resolution.We combined optical and SAR images,based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine,to map and explore the glacier distribution and changes in the Tianshan in 2001,2011,and 2021.Random forest was the best performing classifier,and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a,while among the sub-regions,Dzhungarsky Alatau,Central and Northern/Western Tianshan,and Eastern Tianshan showed a slower,stable,and sharp increase rates after 2011,respectively.Glacier retreat was more severe in the mountain periphery,low plains and valleys,with more area lost near the glacier equilibrium line.The sustained increase in summer temperatures was the primary driver of accelerated glacier retreat.Our work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth Engine.Its high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.展开更多
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 Key Research Program of Frontier Sciences, the Chinese Academy of Sciences (Grant No. QYZDB-SSW-DQC005)the Thousand Youth Talents Plan
文摘Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems（Verburg et al.,2015）.
基金National Natural Science Foundation of China,No.41830105,No.42011530120。
文摘Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high resolution.We combined optical and SAR images,based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine,to map and explore the glacier distribution and changes in the Tianshan in 2001,2011,and 2021.Random forest was the best performing classifier,and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a,while among the sub-regions,Dzhungarsky Alatau,Central and Northern/Western Tianshan,and Eastern Tianshan showed a slower,stable,and sharp increase rates after 2011,respectively.Glacier retreat was more severe in the mountain periphery,low plains and valleys,with more area lost near the glacier equilibrium line.The sustained increase in summer temperatures was the primary driver of accelerated glacier retreat.Our work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth Engine.Its high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.
