An Adaptive Method of High Accuracy Surface Modeling and Its Application to Simulating Elevation Surfaces

An adaptive method is employed to speed up computation of high accuracy surface modeling (HASM), for which an error indicator and an error estimator are developed. Root mean‐square error (RMSE) is used as the error estimator that is formulated as a function of gully density and grid cell size. The error indicator is developed on the basis of error surfaces for different spatial resolutions, which are interpolated in terms of the absolute errors calculated at sampled points while paying attention to the landform characteristics. The error surfaces indicate the magnitude and distribution of errors in each step of adaptive refinement and make spatial changes to the errors in the simulation process visualized. The adaptive method of high accuracy surface modeling (HASM‐AM) is applied to simulating elevation surface of the Dong‐Zhi tableland with 27.24 million pixels at a spatial resolution of 10 m × 10 m. Test results show that HASM‐AM has greatly speeded up computation by avoiding unnecessary calculations and saving memory. In addition, HASM‐AM improves simulation accuracy.     

高精度曲面模型的解算

为了提高Gass-Seidel(GS)算法的收敛速度,提出了改进的GS算法(MGS),用于解算高精度曲面模型(HASM)(HASM-MGS)。以高斯合成曲面为研究对象,将HASM-MGS与HASM-GS和Matlab提供的函数进行对比,结果表明,达到相同的模拟中误差,HASM-MGS计算时间远小于HASM-GS和Matlab提供的函数;HASM-MGS计算时间与模拟区域的网格数呈非常好的线性关系,时间复杂度比传统的方法降低两个数量级。     

基于Google软件的农地区高程获取及精度评价

相比于传统大比例尺测绘,高分遥感和雷达技术可以快速高效地获取农地地形边界和特征点位高程,因而可为农地整理和机械化前期研究提供决策依据。本研究基于Google Maps高分辨率遥感影像数字化农地特征点位和边界,开发了在Google Earth(谷歌地球)软件上获取坐标点位高程的程序,实现了农地特征点位数字化和高程获取;使用国家1:1万数字地形图对该方法进行了精度评估,并且与直接基于SRTM3和ASTER数字高程模型插值方法进行了比较。结果表明,本研究实现了高分辨率遥感影像和数字地形模型的整合应用,在农地边界和特征点位获取、影像校正方面比直接通过SRTM3数据插值获取高程有优势,有较好的应用潜力。     

规则格网DEM地形表面重构的形态精度研究

规则格网 DEM 是数字地形模拟的主要数据结构,以黄土丘陵地区1:50000规则格网 DEM数据为例,分析研究双线性内插模型和三次卷积内插模型在规则格网 DEM 地形表面重构中的形态精度问题。结果表明,不同内插模型构建的细小格网 DEM,在高程数值精度方面没有显著差异,但对局部地形的形态精度具有显著的影响,且双线性内插模型和三次卷积内插模型都难以实现形态高保真 DEM 地形表面重构;随着 DEM 应用的深入,非常有必要构建一些科学合理、简洁高效的形态高保真 DEM 地形表面重构模型。     

An Adaptive Method of Non‐stationary Variogram Modeling for DEM Error Surface Simulation

Geostatistical characterization of local DEM error is usually based on the assumption of a stationary variogram model which requires the mean and variance to be finite and constant in the area under investigation. However, in practice this assumption is appropriate only in a restricted spatial location, where the local experimental variograms vary slowly. Therefore, an adaptive method is developed in this article to model non‐stationary variograms, for which the estimator and the indicator for characterization of spatial variation are a Voronoi map and the standard deviation of mean values displayed in the Voronoi map, respectively. For the adaptive method, the global domain is divided into different meshes with various sizes according to the variability of local variograms. The adaptive method of non‐stationary variogram modeling is applied to simulating error surfaces of a LiDAR derived DEM located in Sichuan province, China. Results indicate that the locally adaptive variogram model is more accurate than the global one for capturing the characterization of spatial variation in DEM errors. The adaptive model can be considered as an alternative approach to modeling non‐stationary variograms for DEM error surface simulation.     

A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local Density

Spatial anomalies may be single points or small regions whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighborhoods. In this article, a S patial A nomaly P oints and R egions D etection method using multi‐constrained graphs and local density ( SAPRD for short) is proposed. The SAPRD algorithm first models spatial proximity relationships between spatial entities by constructing a Delaunay triangulation, the edges of which provide certain statistical characteristics. By considering the difference in non‐spatial attributes of adjacent spatial entities, two levels of non‐spatial attribute distance constraints are imposed to improve the proximity graph. This produces a series of sub‐graphs, and those with very few entities are identified as candidate spatial anomalies. Moreover, the spatial anomaly degree of each entity is calculated based on the local density. A spatial interpolation surface of the spatial anomaly degree is generated using the inverse distance weight, and this is utilized to reveal potential spatial anomalies and reflect their whole areal distribution. Experiments on both simulated and real‐life spatial databases demonstrate the effectiveness and practicability of the SAPRD algorithm.     

Multi‐Scale Qualitative Location: A Topology‐Based Model

Qualitative locations describe spatial objects by relating the spatial objects to a frame of reference (e.g. a regional partition in this study) with qualitative relations. Existing models only formalize spatial objects, frames of reference, and their relations at one scale, thus limiting their applicability in representing location changes of spatial objects across scales. A topology‐based, multi‐scale qualitative location model is proposed to represent the associations of multiple representations of the same objects with respect to the frames of reference at different levels. Multi‐scale regional partitions are first presented to be the frames of reference at multiple levels of scale. Multi‐scale locations are then formalized to relate multiple representations of the same objects to the multiple frames of reference by topological relations. Since spatial objects, frames of reference, and topological relations in qualitative locations are scale dependent, scale transformation approaches are presented to derive possible coarse locations from detailed locations by incorporating polygon merging, polygon‐to‐line and polygon‐to‐point operators.     

DEM制作检查及精度问题的解决方法

介绍了通过数字化仪手扶跟踪及扫描仪半自动采集现有地形图等高线的步骤,再通过内插方法生成DEM,并对如何提高DEM精度提出了解决方案。     

A Multi‐Objective,Multi‐Criteria Approach to Improve Situational Awareness in Emergency Evacuation Routing Using Mobile Phone Data

Emergency services personnel face risks and uncertainty as they respond to natural and anthropogenic events. Their primary goal is to minimize the loss of life and property, especially in neighborhoods with high population densities, where response time is of great importance. In recent years, mobile phones have become a primary communication device during emergencies. The portability of cell phones and ease of information storage and dissemination has enabled effective implementation of cell phones by first responders and one of the most viable means of communication with the population. Using cellular location data during evacuation planning and response also provides increased awareness to emergency personnel. This article introduces a multi‐objective, multi‐criteria approach to determining optimum evacuation routes in an urban setting. The first objective is to calculate evacuation routes for individual cell phone locations, minimizing the time it would take for a sample population to evacuate to designated safe zones based on both distance and congestion criteria. The second objective is to maximize coverage of individual cell phone locations, using the criteria of underlying geographic features, distance and congestion. In summary, this article presents a network‐based methodology for providing additional analytic support to emergency services personnel for evacuation planning.     

Using Pattern‐oriented Modeling (POM) to Cope with Uncertainty in Multi‐scale Agent‐based Models of Land Change

Local land‐use and ‐cover changes (LUCCs) are the result of both the decisions and actions of individual land‐users, and the larger global and regional economic, political, cultural, and environmental contexts in which land‐use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land‐use decisions is a substantial challenge to formulating multi‐scale agent‐based models (ABMs) of land change. Pattern‐oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process‐based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent‐based model of LUCC to design and test model relationships linking global market influence to agents’ land‐use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land‐use outcomes. This framework is promising as an agent‐based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land‐use system and location.     

融合多源数据的三维建模方法及精度分析

以地面相机影像、低空无人机影像和地面三维激光点云数据为融合对象,研究空地影像融合、低空影像融合地面三维激光点云和低空影像融合三维白模进行三维建模的方法,并对其三维建模精度和建模效果进行综合分析。这3种融合方法进行三维建模丰富了三维模型的色彩纹理信息,提高了三维模型的几何结构精度,能满足较大场景的三维实景模型的建模要求,为数字化古建筑(古文物)保护提供具有较高价值的参考数据。     

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.     

A Meta‐Analysis on the Return on Investment of Geospatial Data and Systems: A Multi‐Country Perspective

The availability, quality and accessibility of Geographic Information (GI) have significant socio‐economic and environmental benefits, but the collection and maintenance of GI require substantial investments. Cost‐benefit assessments (CBAs) attempt to justify the costs of geospatial data investments, applying different methodologies and focusing on diverse areas. Therefore, the Returns on Investment (ROI) vary considerably across studies, regions and sectors. The objective of this study is to explain some of the variation in the average ROI of GI by conducting a meta‐analysis of 82 cost‐benefit assessments between 1994 and 2013. In a first step, CBAs are systematically reviewed and relevant information is extracted. Particular emphasis is given to investment conditions and study characteristics. In a second step, multivariate regression methods are used to assess the size, significance and direction of individual effects. The results suggest that regional factors have the largest impact on the profitability of GI. Returns in Australia and New Zealand, for example, are four times larger than in Europe. In addition, small‐scale regional investments have a 2.5 times lower return than large‐scale international investments. Overall, the expected benefits of GI investments are approximately 3.2 times larger than the costs.     

一种新的土方量测算方法及其精度分析

土方计算与工程造价和其方案比选直接相关。提出并分析了一种无须实测地形就可计算土方量方法。使用Google Earth高程数据采集工具,按指定间距和边界采集地形特征点数据,并生成等高线图。使用南方CASS DTM方法计算土方量,并与后期实测1:500地形图的计算结果比对,分析了其偏差。结果显示该方法满足土方量概算要求,可以提供最优的场平标高。该方法在新建工程勘测设计中具有普遍实用性,具备推广意义。     

Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach

A nautical chart provides a schematic view of the seafloor where isobaths (contour lines joining points of same depth) and depth soundings are generalized to highlight undersea features that form navigational hazards and routes. Considering that the process is ultimately driven by features and their significance to navigation, this article proposes a generalization strategy where isobath generalization is controlled by undersea features directly. The seafloor is not perceived as a continuous depth field but as a set of discrete features composed by groups of isobaths. In this article, generalization constraints and operators are defined at feature level and composed of constraints and operators applying to isobaths. In order to automate the process, a multi‐agent system is designed where features are autonomous agents evaluating their environment in order to trigger operations. Interactions between agents are described and an example on a bathymetric database excerpt illustrates the feasibility of the approach.     

基于规则格网的DEM建模方法研究

利用规则格网进行逐点DEM内插的关键在于:如何提高待插点周围数据的搜索效率及如何选取内插函数模型。针对以上两个问题,本文提出了"逐步扩张搜索"法,能迅速搜索到内插点所需的参考点数据;并在分析和比较各种DEM内插算法的基础上,结合二次曲面模型和加权平均法的优点,提出"组合内插模型"。实验表明,改进后的DEM逐点内插算法,能显著提高搜索速度和内插精度,具有很好的应用价值。     

基于Coons曲面的规则格网DEM表面模型

内插是数字高程模型的核心问题。目前的内插模型主要是由离散的格网数据构建的连续曲面,直接以点推面,可能存在较大的地形误差。本文建立的Coons曲面DEM表面模型,首先利用离散的格网数据构造与格网边界相对应的地形剖面曲线的拟合曲线,再基于拟合曲线构建DEM表面模型。实验表明：Coons曲面DEM表面模型是一种高精度的DEM表面模型,其地形模拟误差比直接基于格网数据建立的双线性内插、样条函数内插和移动曲面拟合法的误差都小,实际地形模拟误差与双线性模型相比减少15％-28％,且精度随着构建边界拟合曲线所用格网点的增多而逐渐提高。     

基于生产的高精度海岸线提取方法

介绍了海岸线的定义及几种常用的海岸线测量方法,重点比较了两种基于正射影像结合验潮数据获取高精度海岸线方法的优缺点,并对特殊区域的海岸线划定进行具体说明。     

利用SAR影像配准偏移量提取地表形变的方法与误差分析

单一的InSAR观测技术可提取地表沿雷达视线方向(LOS)上的一维位移,而利用SAR影像配准过程中的同名像素偏移量可提取地表沿雷达方位向(近南北向)与距离向(近东西向)的二维形变场,与LOS方向的一维形变形成优势互补。本文在分析SAR影像配准偏移量提取地表形变场方法的基础上,推导建立了雷达方位向与距离向形变提取的误差模型,探讨了该方法提取地表形变的主要误差源。以Bam地震ASAR影像和玉树地震PALSAR影像为数据源,分别开展同震形变场的提取与误差分析试验,结果表明,基于SAR像素配准偏移量提取同震形变场的精度主要受匹配窗口尺寸与过采样因子影响,形变提取误差随匹配计算窗口的增大而减小,形变提取精度随过采样因子的增大有适量提高,地形起伏效应在高差较大的SAR影像区域中表现较为显著。     

宁夏北部地区高精度似大地水准面的确定

本文介绍了利用重力点成果、30″×30″分辨率数字高程模型;360阶次的国内外先进的重力场模型(EGM96、WDM94等)及分布较均匀的、现势性较好的GPS网及水准测量成果,采用重力法(Stokes、Molodensky原理)及移去～恢复技术、FFT计算方法,比较不同的计算结果,选取最佳成果作为宁夏区域重力大地水准面,然后再和布测的GPS水准所构成的高程异常控制网拟合,完成宁夏中北部区域分辨率为2.5’×2.5’(相当于5km×3Km)、精度为±10cm的大地水准面的计算工作。