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公寓是地籍系统中最具代表性并应以3D形式登记的不动产单元,其建筑结构及空间形态影响着各单元在三维地籍注册中所占据的空间,将房屋几何信息引入地籍系统可实现法定空间的准确表达。本文以ISO/TC 211土地管理域模型LADM为模板,基于地籍管理中"法定空间-物理结构"理论,将房屋物理模型引入LADM,以房屋或建筑物物理实体模型或几何信息为空间参照,在三维地籍系统中实现公寓单元"法定空间"的准确表达与登记。 相似文献
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The research presented in this article is based on a new technique governed by three different statistical indicators determined for each causative parameter, viz. highest density, average density and co-efficient of variation of landslides. Each of these indicators was assigned a rank value between 1 and 14 depending upon its variation among the 14 causative parameters. The aggregate of the three types of rank values estimate the total ranking value (TRV) for each causative parameter. The study area is divided into 78,256 spatial units and for each such spatial unit, the influence of the different causative parameters is determined as the product of the experts' weight of the associated sub-category and the TRV of the causative parameter that categorizes the study area into various zones. The efficacy of the proposed technique is demonstrated by the occurrence of significantly high prediction accuracy of 84%. 相似文献
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面向图层处理单元的GIS数据模型、数据处理模式已不再适应大规模海量空间数据组织、处理以及网络分发的需要。文章提出以空间要素基元处理取代传统的面向图层处理的模式,并在对象-关系数据库(ORDBMS)统一框架下构筑、组织、存储、处理空间数据,最终形成基于ORDBMS的GIS应用。 相似文献
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《制图学和地理信息科学》2013,40(4):239-263
The objective of this paper is to present a spatially explicit agent-based simulation framework with a supporting software package to explore complex adaptive geographic systems. This framework is particularly suitable for modeling entities that are contextually aware, knowledge driven, and adaptive because it represents them as geographically aware intelligent agents. Fundamental advances in the explicit representation of contextual information, knowledge structures, and learning processes are needed for modeling intelligent agents situated within geographic systems. The representation of these agents requires the integration of agent-based models, machine learning, and GIS. Existing software packages for agent-based modeling, however, often provide insufficient support for this integration. The agent-based simulation package presented here is specifically designed to achieve such integration by assisting the development of agent-based models from the simulation framework. Object-oriented modeling techniques were used to implement this simulation package, which includes four modules: simulation, visualization, learning, and geoprocessing. In particular, the learning and geoprocessing modules facilitate the representation of adaptive behavior in agents within spatially explicit environments. The utility of the agent-based simulation package is illustrated using two simulation models: one of adaptive elk behavior and another of pedestrian movement. The successful design of the simulation models suggests that the modeling framework with the supporting software package is well suited to the resolution of complex adaptive geographic problems. 相似文献
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在对移动GIS技术和框架分析的基础上,选取GeoServer地理信息发布平台,进行移动GIS旅游系统功能设计;采用OGC提出的空间数据互操作规范WMS、WFS实现地理信息数据交换;以七台河旅游数据作为专题数据,实现移动端旅游景点查询与语音导游系统。 相似文献
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基于误差补偿预测树的多光谱遥感图像无损压缩方法 总被引：6，自引：0，他引：6
预测树方法是一种有效的无损多光谱图像压缩技术,将自适应线性预测方法与传统预测树方法相结合,提出了一种多光谱遥感图像的误差补偿预测树压缩方法。该方法利用多光谱图像谱间的局部统计冗余和结构冗余建立自适应预测器,对传统预测树方法产生的误差进行补偿,从而进一步减少了多光谱图像的数据量;并且利用多光谱图像的局部平稳特性对算法进行了简化。实验结果表明,该方法得到的压缩比与原始预测树方法相比有明显提高,同时算法简化后可以使计算复杂度大幅度降低。 相似文献
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This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches. 相似文献
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针对影像区域匹配方法几何形变敏感的应用局限性,将特征匹配、相位匹配的基本思想引入区域匹配过程,基于傅立叶-梅林-仿射两级变换建立了归一化互相关灰度相似性计算下的自适应模板匹配框架,并详细阐述了该框架下的全局运动估计、模板"粗"纠正与搜索预测、局部仿射变换下的"精"模板动态生成等关键过程与算法。实验证明了该方法的有效性。 相似文献
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Integration of spatial and spectral information is an effective way in improving classification accuracy. In this article a new framework, based on multi-scale spatial weighted mean filtering (MSWMF) and minimum spanning forest, is proposed for the spectral–spatial classification of hyperspectral images. In the proposed framework, at first the image is smoothed by MSWMF and then the first eight principal components are extracted. Using support vector machine, at each scale of MSWMF, a classification map is produced in order to generate a marker map in the next step. Then, the minimum spanning forest is built on the marker map. Finally, in order to create a final classification map, all the classification maps of each scale are merged with a majority vote rule. The experimental results of the hyper-spectral images indicate that the suggested framework enhances the classification accuracy, in comparison with previously classification techniques. So, it is interesting for hyperspectral images classification. 相似文献
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A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis 总被引：2，自引：0，他引：2
Yu Liu Qinghua Guo Maggi Kelly 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):461-475
Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. 相似文献
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空间认知驱动的自适应路径引导 总被引：1，自引：0，他引：1
为了生成符合人们认知习惯、反映用户空间知识并易于利用自然语言表达的路径引导, 提出了一个将路径抽象为一系列结构统一、具有时序性和多粒度性且可以被加工为指导用户沿路径前进的短语或句子的指示单元的表达框架,并说明了利用环境结构、路径特征、先验知识等上下文因素生成多粒度的指示单元, 从中选择最合适的指示单元, 进而实现自适应路径引导的方法。通过与传统的采用“Distance-to-Turn”模式的路径引导进行对比可以发现, 基于空间认知的自适应路径引导更加符合人们描述路径的方式, 能够降低用户的认知压力并提高导航的效率。 相似文献
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ABSTRACT Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations. 相似文献
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“二级检查一级验收”作为测绘单位质量控制的基本制度,在成果质量保障中发挥着重要作用,但是部分单位因整改质量不高,造成在最终检查环节上执行不顺畅。通过质检实践,总结了常规最终检查模式下整改中存在的问题,针对基础测绘项目中整改效果不明显的实际情况,在常规检查模式的基础上提出了基于检查信息分层的粗反馈最终检查模式,并通过应用实例验证了该模式在提升整改质量中的有效性和实用性。 相似文献
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Improving prediction performance of GPS satellite clock bias based on wavelet neural network 总被引：2，自引：0，他引：2
As one of the IGS ultra-rapid predicted (IGU-P) products, the orbit precision has been remarkably improved since late 2007. However, because satellite atomic clocks in space show complicated time–frequency characteristics and are easily influenced by many external factors such as temperature and environment, the IGU-P clock products have not shown sufficient high-quality prediction performance. An improved prediction model is proposed in order to enhance the prediction performance of satellite clock bias (SCB) by employing a wavelet neural network (WNN) model based on the data characteristic of SCB. Specifically, two SCB values of adjacent epoch subtract each other to get the corresponding single difference sequence of SCB, and then, the sequence is preprocessed through using the preprocessing method designed for the single difference sequence. The subsequent step is to model the WNN based on the preprocessed sequence. After the WNN model is determined, the next single difference values at the back of the modeling sequence are predicted. Lastly, the predicted single difference values are restored to the corresponding predicted SCB values. The simulation results have shown that the proposed prediction principle based on the single difference sequence of SCB can make the WNN model simple in architecture and the predicting precision higher than that of the general SCB prediction modeling. The designed preprocessing method specific to the single difference of SCB is able to further improve the prediction performance of the WNN model by reducing the effect from outliers. The proposed SCB prediction model outperforms the IGU-P solutions at least on a daily basis. Specifically, the average prediction precisions for 6, 12 and 24 h based on the proposed model have improved by about 13.53, 31.56 and 49.46 % compared with the IGU-P clock products, and the corresponding average prediction stabilities for 12 and 24 h have increased by about 13.89 and 27.22 %, while the average prediction stability of 6 h is nearly equal. 相似文献
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This study proposes network‐based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross‐validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network‐based methods and their conventional counterparts. The study suggests that both network‐based IDW and network‐based OK are generally more accurate than their existing counterparts, with network‐based OK constantly outperforming the other methods. The network‐based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network‐based OK improve as more sample locations are used, whereas those of standard IDW and network‐based IDW remain stable regardless of the number of sample locations. The two network‐based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations. 相似文献
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Forests play a critical role in sustaining the human environment. Most forest fires not only destroy the natural environment and ecological balance, but also seriously threaten the security of life and property. The early discovery and forecasting of forest fires are both urgent and necessary for forest fire control. This article explores the possible applications of Spatio‐temporal Data Mining for forest fire prevention. The research pays special attention to the spatio‐temporal forecasting of forest fire areas based upon historic observations. An integrated spatio‐temporal forecasting framework – ISTFF – is proposed: it uses a dynamic recurrent neural network for spatial forecasting. The principle and algorithm of ISTFF are presented, and are then illustrated by a case study of forest fire area prediction in Canada. Comparative analysis of ISTFF with other methods shows its high accuracy in short‐term prediction. The effect of spatial correlations on the prediction accuracy of spatial forecasting is also explored. 相似文献
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人口统计数据的空间分布化研究 总被引：21，自引：0，他引：21
分析了传统的人口空间分布密度衰减函数－指数型和Gauss型，指出了其应用的局限性，对于有两个中心以上的城市，提出了将人口统计数据空间分布化的思路和方法。 相似文献