High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HAS...High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HASM-SP） to improve the accuracy. Based on soil types, land use types and parent rocks, HASM-SP was applied to interpolate soil available P, Li, pH, alkali-hydrolyzable N, total K and Cr in a typical red soil hilly region. To evaluate the performance of HASM-SP, we compared its performance with that of ordinary kriging （OK）, ordinary kriging combined geographic information （OK-Geo） and stratified kriging （SK）. The results showed that the methods combined with geographic information including HASM-SP and OK-Geo obtained a lower estimation bias. HASM-SP also showed less MAEs and RMSEs when it was compared with the other three methods （OK-Geo, OK and SK）. Much more details were presented in the HASM-SP maps for soil properties due to the combination of different types of geographic information which gave abrupt boundary for the spatial varia- tion of soil properties. Therefore, HASM-SP can not only reduce prediction errors but also can be accordant with the distribution of geographic information, which make the spatial simula- tion of soil property more reasonable. HASM-SP has not only enriched the theory of high accuracy surface modeling of soil property, but also provided a scientific method for the ap- plication in resource management and environment planning.展开更多
The miniaturization of transistors led to advances in computers mainly to speed up their computation.Such miniaturization has approached its fundamental limits.However,many practices require better computational resou...The miniaturization of transistors led to advances in computers mainly to speed up their computation.Such miniaturization has approached its fundamental limits.However,many practices require better computational resources than the capabilities of existing computers.Fortunately,the development of quantum computing brings light to solve this problem.We briefly review the history of quantum computing and highlight some of its advanced achievements.Based on current studies,the Quantum Computing Advantage(QCA)seems indisputable.The challenge is how to actualize the practical quantum advantage(PQA).It is clear that machine learning can help with this task.The method used for high accuracy surface modelling(HASM)incorporates reinforced machine learning.It can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd(HHL)quantum algorithm to support quantum machine learning.HASM has been successfully used with classical computers to conduct spatial interpolation,upscaling,downscaling,data fusion and model-data assimilation of ecoenvironmental surfaces.Furthermore,a training experiment on a supercomputer indicates that our HASM-HHL quantum computing approach has a similar accuracy to classical HASM and can realize exponential acceleration over the classical algorithms.A universal platform for hybrid classical-quantum computing would be an obvious next step along with further work to improve the approach because of the many known limitations of the HHL algorithm.In addition,HASM quantum machine learning might be improved by:(1)considerably reducing the number of gates required for operating HASM-HHL;(2)evaluating cost and benchmark problems of quantum machine learning;(3)comparing the performance of the quantum and classical algorithms to clarify their advantages and disadvantages in terms of accuracy and computational speed;and(4)the algorithms would be added to a cloud platform to support applications and gather active feedback from users of the algorithms.展开更多
Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high r...Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.展开更多
基金Foundation： National Natural Science Foundation of China, No.41001057 China National Science Fund for Distinguished Young Scholars, No.40825003 Project Supported by State Key Laboratory of Earth Surface Processes and Resource Ecology, No.2011-KF-06
文摘High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HASM-SP） to improve the accuracy. Based on soil types, land use types and parent rocks, HASM-SP was applied to interpolate soil available P, Li, pH, alkali-hydrolyzable N, total K and Cr in a typical red soil hilly region. To evaluate the performance of HASM-SP, we compared its performance with that of ordinary kriging （OK）, ordinary kriging combined geographic information （OK-Geo） and stratified kriging （SK）. The results showed that the methods combined with geographic information including HASM-SP and OK-Geo obtained a lower estimation bias. HASM-SP also showed less MAEs and RMSEs when it was compared with the other three methods （OK-Geo, OK and SK）. Much more details were presented in the HASM-SP maps for soil properties due to the combination of different types of geographic information which gave abrupt boundary for the spatial varia- tion of soil properties. Therefore, HASM-SP can not only reduce prediction errors but also can be accordant with the distribution of geographic information, which make the spatial simula- tion of soil property more reasonable. HASM-SP has not only enriched the theory of high accuracy surface modeling of soil property, but also provided a scientific method for the ap- plication in resource management and environment planning.
基金supported by the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022ORP02)the National Natural Science Foundation of China(Grant Nos.41930647,72221002)the Key Project of Innovation LREIS(Grant No.KPI005).
文摘The miniaturization of transistors led to advances in computers mainly to speed up their computation.Such miniaturization has approached its fundamental limits.However,many practices require better computational resources than the capabilities of existing computers.Fortunately,the development of quantum computing brings light to solve this problem.We briefly review the history of quantum computing and highlight some of its advanced achievements.Based on current studies,the Quantum Computing Advantage(QCA)seems indisputable.The challenge is how to actualize the practical quantum advantage(PQA).It is clear that machine learning can help with this task.The method used for high accuracy surface modelling(HASM)incorporates reinforced machine learning.It can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd(HHL)quantum algorithm to support quantum machine learning.HASM has been successfully used with classical computers to conduct spatial interpolation,upscaling,downscaling,data fusion and model-data assimilation of ecoenvironmental surfaces.Furthermore,a training experiment on a supercomputer indicates that our HASM-HHL quantum computing approach has a similar accuracy to classical HASM and can realize exponential acceleration over the classical algorithms.A universal platform for hybrid classical-quantum computing would be an obvious next step along with further work to improve the approach because of the many known limitations of the HHL algorithm.In addition,HASM quantum machine learning might be improved by:(1)considerably reducing the number of gates required for operating HASM-HHL;(2)evaluating cost and benchmark problems of quantum machine learning;(3)comparing the performance of the quantum and classical algorithms to clarify their advantages and disadvantages in terms of accuracy and computational speed;and(4)the algorithms would be added to a cloud platform to support applications and gather active feedback from users of the algorithms.
文摘Ground control point (GCP) is important for georeferencing remotely sensed images and topographic model. However, considering that GCP collection is sometimes a difficult, time-consuming and expensive task with high resolution (HR) data in remote and harsh environments, today unmanned aerial vehicle based remote sensing (UAVRS) is frequently used in geological disaster emergency monitoring and rescuing for its great advantage in collecting timely onsite images. In this paper, for evaluating the feasibility of the UAVRS in disaster emergency and high cut slope safety monitoring, the digital surface model (DSM) without GCPs based on Structure from Motion (SfM) is accessed, and results showed that the geometric accuracy of DSM was smaller than 1 percent, which prove the usefulness of DSM based on UAVRS in emergency. Comparing to normal disaster emergency, the method without GCPs can be more efficient and save the disaster emergency time by neglecting GCPs measurement.
