Application and prospects of spatial information technology in CO_(2)sequestration monitoring

This paper systematically reviews the current applications of various spatial information technologies in CO_(2)sequestration monitoring,analyzes the challenges faced by spatial information technologies in CO_(2)sequestration monitoring,and prospects the development of spatial information technologies in CO_(2)sequestration monitoring.Currently,the spatial information technologies applied in CO_(2)sequestration monitoring mainly include five categories:eddy covariance method,remote sensing technology,geographic information system,Internet of Things technology,and global navigation satellite system.These technologies are involved in three aspects:monitoring data acquisition,positioning and data transmission,and data management and decision support.Challenges faced by the spatial information technologies in CO_(2)sequestration monitoring include:selecting spatial information technologies that match different monitoring purposes,different platforms,and different monitoring sites;establishing effective data storage and computing capabilities to cope with the broad sources and large volumes of monitoring data;and promoting collaborative operations by interacting and validating spatial information technologies with mature monitoring technologies.In the future,it is necessary to establish methods and standards for designing spatial information technology monitoring schemes,develop collaborative application methods for cross-scale monitoring technologies,integrate spatial information technologies with artificial intelligence and high-performance computing technologies,and accelerate the application of spatial information technologies in carbon sequestration projects in China.

Developments of spatial information-based Digital Mine in China

Gave a brief introduction to the origin,concepted and hierarchical structure of Digital Mine.As a huge complex system,Digital Mine took data base and model base to- gether as a mine data management system being its core,and Digital Mine was com- prised of five subsystems including data obtaining system,integral dispatching system, applied engineering system,data processing system,and data management system.Be- ing a digitally 3D visualized representation and a spatial information infrastructure of an actual mine,Digital Mine had three basic features such as data warehouse,information reference and digital platform.The present developments of Digital Mine in mining industry, research and education were also introduced.Examples were shown for present Digital Mine construction in China.The development trends,the key technologies and the recent construction procedures on Digital Mine were presented.

Spatial Information Research for Temperature and Precipitation Climate Data in Hengduan Mountains

[Objective] The aim was to study the spatial information of temperature and precipitation data in Hengduan mountains. [Method] Considering GIS spatial interpolation and numerical statistics theory, spatial prediction were carried out to the ten years average temperature and precipitation flux observation data in 109 sparse meteorological stations in Hengduan Mountains. Based on the spatial range of geographic position of Hengduan Mountains, and 1∶1 000 000 scale DEM as data sources, and using trend surface simulation and residual ordinary Kriging interpolation correction method, the spatial continuous surface for annual average temperature and precipitation were studied. [Result] It was scientific and reasonable to use certain unevenly distributed sparse climate observation station value, and by dint of trend simulation and residue interpolation method to get climate consecutive spatial result. This method can not only accurate the temperature and precipitation spatial distributions to grid point, but also can reflect macro and micro geography factors and topographic influence factor of variation. Furthermore, it can be predicted error term trend surface reasonable spatial distribution. Simulation results were basically in accordance with the objective law, and can be used for the region climate data spatial informatization simulation. [Conclusion] The study provided scientific spatial basic data for the further study of ecological and vegetation in Hengduan Mountains.

Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Full feature data model for spatial information network integration

In allusion to the difficulty of integrating data with different models in integrating spatial information, the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vector-raster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid, were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.

Neural Network Optimization of Multivariate KDE Bandwidth for Buoy Spatial Information

It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning.If the position deviation of the light buoy is too large to be detected in time,sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department.Therefore,mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy.Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position,and indicates the intensive areas of buoy position in the channel.In this paper,in order to speed up deciding the optimal variable width of kernel density estimator,an improved adaptive variable width kernel density estimator is proposed,which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density.A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator.It not only achieves faster training speed,but also improves the estimation accuracy of probability density.

Web service based spatial forest information system using an open source software approach

For technical and other reasons there is a dilemma that data providers cannot find an appropriate way to redistribute spatial forest data and data users who need spatial data cannot access and integrate available forest resources information. To overcome this dilemma, this paper proposed a spatial forest information system based on Web service using an open source software approach. With Web service based architecture, the system can enable interoperability, integrate Web services from other application servers, reuse codes, and shorten the development time and cost. At the same time, it is possible to extend the local system to a regional or national spatial forest information system. The growth of Open Source Software （OSS） provides an alternative choice to proprietary software for operating systems, web servers, Web-based GIS applications and database management systems. Using open source software to develop spatial forest information systems can greatly reduce the cost while providing high performance and sharing spatial forest information. We chose open source software to build a prototype system for Xixia County, Henan Province, China. By integrating OSS packages Deegree and UMN MapServer which are compliant to the OGC open specifications, the prototype system enables users to access spatial forest information and travelling information of Xixia County which come from two different data servers via a standard Web browser and promotes spatial forest information sharing.

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

Design and Implementation of Service-Oriented Spatial Information Sharing Framework in Digital City

This paper proposes a theoretical framework of spatial information sharing in a digital city, and analyzes its technical characteristics. According to the service-oriented architecture (SOA) framework, a geospatial information sharing platform is put forward. The spatial information sharing model based on SOA is designed. A prototype platform realizing multiple-source spatial information sharing based on ArcGIS Server is developed.

Optically spatial information selection with hybridly polarized beam in atomic vapor

Vector beams with spatially variant polarization have attracted much attention in recent years, with potential applications in both classical optics and quantum optics. In this work, we study a polarization selection of spatial intensity distribution by utilizing a hybridly polarized beam as a coupling beam and a circularly polarized beam as a probe beam in87 Rb atom vapor. We experimentally observe that the spatial intensity distribution of the probe beam after passing through atoms can be modulated by the hybridly polarized beam due to the optical pumping effect. Then, the information loaded in the probe beam can be designedly filtrated by an atomic system with a high extinction ratio. A detailed process of the optical pumping effect in our configurations and the corresponding absorption spectra are presented to interpret our experimental results, which can be used for the spatial optical information locally extracted based on an atomic system, which has potential applications in quantum communication and computation.

On generalised and specialised spatial information grids: are geo-services ready?

After the set-up of a spatial data infrastructure(SDI)and a national information infrastructure(NII)in many countries,the provision of geo-services became one of the most important and attractive tasks.With the integration of global positioning system(GPS),geographic information system(GIS)and remote sensing(RS),we can,in principle,answer any geo-spatial related question:when and where what object has which changes?An intelligent geo-service agent could provide end-users with the most necessary information in the shortest time and at the lowest cost.Unfortunately there is still a long way to go to achieve such goals.The central component in such geo-services is the integration of the spatial information system with a computing grid via wire-and wireless communication networks.This paper will mainly discuss the grid technology and its integration with spatial information technology,expounding potential problems and possible resolutions.A novel categorising of information grids in the context of geospatial information is proposed:generalised and specialised spatial information grids.

Digital humanities affect: The evolution and revolution of spatial information organization in science fiction

The spatial information of literature refers to the representation of physical or conscious space in literary works,including position,shape,structure,features,and relationships.The spatial information organization and its method are essential to the whole process of literature production,circulation,acceptance,and reproduction,which is conducive to a deeper exploration of the aesthetic essence of literature in the information era.Taking the organization of spatial information in science fiction as an example,this paper investigates the way of interaction between informatics and literature,explores the function of spatial information in the narrative of science fiction,and discovers digital humanistic laws of literary information organization.This study finds that the spatial information of early science fiction is organized in horizontal,vertical,axial,and causal modes;in the era of digital humanities,influenced by information technology such as the internet,virtual reality,and artificial intelligence,literary space has experienced revolutions characteristic of time-space integration,boundary dissolution,dimensional change,and virtualization;the spatial information organization of contemporary science fiction adopts nested,reticulated,parallel,interactive,and reverse patterns;on the whole,it is featured with fluidity,chaos,correlation,and mutation,creating a unique digital humanistic spectacle.

Optimization of multi-state generation problem based on spatial information network topology

Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology change.With the increasing number of low-orbit satellites,the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information network.In this paper,two common objective functions in inter-satellite link assignment,network observation position and network communication factor are studied,and a multi-objective optimization model is constructed.Depth first search,simulated annealing,NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the model.By comparing the solving efficiency of the model through simulation experiments,the difference of the results caused by the four algorithms is verified.

Oceanographic ontology-based spatial knowledge query

The construction of oceanographic ontologies is fundamental to the ＂digital ocean＂. Therefore, on the basis of introduction of new concept of oceanographic ontology, an oceanographic ontology-based spatial knowledge query （OOBSKQ） method was proposed and developed. Because the method uses a natural language to describe query conditions and the query result is highly integrated knowledge, it can provide users with direct answers while hiding the complicated computation and reasoning processes, and achieves intelligent, automatic oceanographic spatial information query on the level of knowledge and semantics. A case study of resource and environmental application in bay has shown the implementation process of the method and its feasibility and usefulness.

Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier （MLC）.

Information Theory of Cartography:An Information-theoretic Framework for Cartographic Communication

Map is one of the communication means created by human being.Cartographers have been making efforts on the comparison of maps to natural languages so as to establish a"cartographic language"or"map language".One of such efforts is to adopt the Shannon’s Information Theory originated in digital communication into cartography so as to establish an entropy-based cartographic communication theory.However,success has been very limited although research work had started as early as the mid-1960 s.It is then found that the bottleneck problem was the lack of appropriate measures for the spatial(configurational)information of(graphic and image)maps,as the classic Shannon entropy is only capable of characterizing statistical information but fails to capture the configurational information of(graphic and image)maps.Fortunately,after over 40-year development,some bottleneck problems have been solved.More precisely,generalized Shannon entropies for metric and thematic information of(graphic)maps have been developed and the first feasible solution for computing the Boltzmann entropy of image maps has been invented,which is capable of measuring the spatial information of not only numerical images but also categorical maps.With such progress,it is now feasible to build the"Information Theory of Cartography".In this paper,a framework for such a theory is proposed and some key issues are identified.For these issues,some have already been tackled while others still need efforts.As a result,a research agenda is set for future action.After all these issues are tackled,the theory will become matured so as to become a theoretic basis of cartography.It is expected that the Information Theory of Cartography will play an increasingly important role in the discipline of cartography because more and more researchers have advocated that information is more fundamental than matter and energy.

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

Underwater sonar image recognition based on gray-spatial histograms

A new gray-spatial histogram is proposed, which incorporates spatial informatio n with gray compositions without sacrificing the robustness of traditional gray histograms. The purpose is to consider the representation role of gray compositi ons and spatial information simultaneously. Each entry in the gray-spatial hist ogram is the gray frequency and corresponding position information of images. In the experiments of sonar image recognition, the results show that the gray-spa tial histogram is effective in practical use.