基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

元胞自动机结构简单且具备模拟复杂系统的能力,已被广泛应用于大气、流体力学、地球物理等领域。然而,现有元胞自动机以欧氏空间为约束进行地球系统过程模拟,忽略了地球重力等天然约束,导致计算过程中元胞状态的传递方向与真实运动的趋势方向不相符,一定程度上扭曲了最终的模拟结果。本文提出了地球系统元胞自动机这一概念,并从元胞表达及构建、邻居模型等方面设计了基于SDOG-ESSG格网的地球系统元胞自动机框架。由于演化规则取决于不同的应用,因此本文进一步以地壳的热传递为例,通过对热力学方程离散化设计了相应的演化规则。最后,借助公开的数据源开展了地壳热传递元胞自动机模拟的初步试验,并与一定区域下的数值模拟结果展开了比对。试验表明,与数值模拟方法相比,本文方法的模拟结果相对误差控制在27%以内,具备一定程度的可行性,可作为地球系统过程模拟的一种新思路。     

结合多尺度纹理特征的遥感影像面向对象分类

地物具有多尺度的特点,单一尺度难以准确描述遥感影像包含的地物纹理信息。利用我国自行研发的高分一号遥感影像数据,采用灰度共生矩阵对第一主成分进行纹理特征提取,利用Jeffries-Matusit距离选择多尺度组合,并通过单一纹理结合多光谱数据的分类精度,以及纹理特征间的相关性,最终选择多尺度纹理特征组合进行面向对象分类。研究结果表明:结合多尺度纹理特征组合的面向对象GF-1影像分类能有效提取地物信息,总体分类精度达到81.75%,Kappa系数0.78。     

基于点云空洞修复和TPS模型的数学形态学机载LIDAR滤波

在分析现有滤波算法的基础上,结合数据驱动和模型驱动式算子各自的优点,提出基于点云空洞修复和TPS变形模型的数学形态学机载LIDAR点云滤波,该方法首先提取和修复由水域造成的大面积点云空洞,采用多尺度形态学开算子作用于修复的数据,得到近似裸露地表面;然后利用2D空间的TPS变形模型,以近似地表面为基础,插值原始点云,根据插值与原始点云高程的差值大小去识别地面点和非地面点。通过定量分析,验证该方法不仅有较高的滤波精度,而且也能较好的保留裸露地表的细节特征,同时该方法有助于辅助人工处理,提高数据处理的质量。     

不动产统一登记信息平台建设探讨

不动产统一登记信息平台的建立是不动产统一登记工作实现信息实时更新和共享的基础。以构建不动产统一登记信息平台为主线,提出了以土地为核心的不动产单元构建方法,构建了以不动产单元为基础的不动产权利登记体系,分析了不动产登记业务与不动产管理其他相关业务的关联。在此基础上,明晰了不动产统一登记信息平台在国土资源信息化总体框架中的定位,以徐州市为例,给出了市(县)级不动产统一登记信息平台建设的主要内容,对当前不动产统一登记信息平台的建设进行了有益的探索。     

Identifying geospatial services across heterogeneous taxonomies

Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach.     

A hybrid approach to modeling territorial control in violent armed conflicts

Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time.     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Approach to calculating spatial similarity degrees of the same river basin networks on multi-scale maps

Obtaining spatial similarity degrees among the same objects on multi-scale maps is of importance in map generalization. This paper firstly defines the concepts of ‘map scale change’ and ‘spatial similarity degree’; then it proposes a model for calculating the spatial similarity degree between a river basin network at one scale and its generalized version at another scale. After this, it validates the new model and gets 16 points in the model validation process. The x-coordinate and y-coordinate of each point are map scale change and spatial similarity degree, respectively. Last, a formula for calculating spatial similarity degree taking map scale change as the only variable is obtained by the curve fitting method. The formula along with the model can be used to automate the algorithms for simplifying river basin networks.     

与找矿信息量法结合的含矿网格单元蒙特卡洛矿产资源潜力评价方法的原理及应用

文章介绍了以含矿网格单元为统计单位、并与找矿信息量法结合的蒙特卡洛矿产资源潜力评价方法的原理和工作步骤,以及用该法进行丹池锡多金属成矿带锡矿矿产资源潜力评价工作案例,表明该法可以把评价工作区的矿产空间分布信息、成矿模式和找矿模型、成矿-找矿空间信息引入蒙特卡洛方法中,无需人类专家估计矿床个数,减少了工作步骤,提高了工作效率和自动化程度,减少了因人为主观性而造成的评价误差。     

横向振动格栅紊流紊动特性试验

建立了一套大尺度格栅紊流试验系统,格栅进行有别于传统垂向模式的横向振动,采用粒子图像测速技术(Particle Image Velocimetry,PIV)测量流速。对瞬时流速的检验表明,该系统产生的紊流场具有较强的随机性和统计规律性。均方根流速在格栅片附近变化较大,在两片格栅中间处趋于稳定,纵向均方根流速明显大于垂向均方根流速,二者比值在1.5~2.0之间,接近天然明渠紊流。雷诺应力在距格栅越近处波动越大,随着距格栅距离的增加而减小,至两片格栅中间处雷诺应力基本为0。时间和长度积分尺度在格栅片处最小,随着距格栅距离的增加而线性增加,至两片格栅中间处达到最大值。流速能谱呈现Kolmogorov理论的-5/3次方规律。本系统生成的紊流场的统计规律与传统的垂向振动格栅紊流较为一致,但纵向和垂向的紊动强度更接近实际,为后续紊流中泥沙和污染物等运动机理的研究奠定了基础。