数字马鞍山3维可视化平台的设计与实现

3维可视化平台是数字城市地理空间框架的重要组成部分,本文介绍了数字马鞍山建设中3维平台的设计与实现思路。从2,3维协同服务的角度设计了3维建设技术路线,并对海量数据传输加载及2,3维联动等关键问题提出了有益的解决方案。为其他城市建设地理空间框架提供借鉴。     

矿区三维地质建模与地裂缝成因分析

为了分析在复杂地质条件下采煤引起的大规模地表裂缝的形成原因,本文以某矿区地裂缝为研究对象、数字化软件SURPAC为技术平台、岩体为主要区域变量,建立关于开采工作面、火成岩、地裂缝及断层的三维地质模型,并进行开采沉陷影响预计,通过分析裂缝成因中各相关地质条件的作用影响,得出了煤炭开采导致地下应力平衡被打破后造成地裂缝的主要因素,为预防和治理地裂缝提供了参考。     

物联网与三维GIS技术在油田设备设施管理中的应用

针对物联网技术和三维GIS技术的特点,阐述了物联网与三维GIS之间的关系。利用油田网络、物联设备之间的传感与自动信息采集,结合三维虚拟场景提供设备设施的空间感、方位感等,使设备设施更具有时空分辨能力,为油田设备精细化管理提供条件。     

基于3S技术的土地开发整理探讨

土地开发整理对于节约土地资源、维持可持续发展具有重要作用,是一项涉及多方面的复杂系统工程。3S技术为土地利用信息获取和土地开发整理提供了必要的技术支撑,本文在总结分析3S技术特点的基础上,设计了基于3S技术的土地开发整理体系,该体系可以为土地开发整理工作提供借鉴。     

潜在连接点集下拓扑推进三维建模算法研究

通过三维激光扫描仪可以获取到表示空间对象的点云数据。通过设置仪器的采样间隔可以获取到表示对象不同精密程度的点集。但是由于离散点之间缺乏相应的拓扑关系,限制了以点为基本图元的模型的应用。以空间离散特征点集为研究对象,研究了一种三维建模算法。算法基于距离较近的点之间比距离较远的点之间存在拓扑连接的可能性更大的前提,对点集进行预处理,然后在此基础上进行快速建模,最终获取到以三角形为基本面片的格网模型。所建立的模型易于进行各项空间分析和操作。该算法也能够有效地应用于更为复杂的建模任务中,以达到一体化建模的目的。     

基于ArcGIS的三维建筑日照时态模拟

以福州大学怡山校区为例,在ArcGIS 10环境中,结合SketchUp构建三维建筑模型,对建筑物接受日照和产生阴影遮挡情况进行三维可视化时态模拟研究,提出了无需插件的MultiPatch三维模型创建思路以及三维建筑的日照分析方案。研究结果不仅可为城建规划部门及居民提供快捷可靠的科学日照分析手段,而且拓展了三维建筑模型在GIS中的应用。     

全球六边形离散格网数据的三维可视化方法

针对全球六边形离散格网的三维显示化方法开展研究,设计了一种六边形格网的空间层次结构(hexagonalquaternary balanced structure,HQBS),采用四位码元对格网单元进行编码,定义并实现了格网向量的基本运算,利用这些运算可以方便地实现格网单元的空间索引。在此基础上还研究了全球离散格网的动态生成与显示算法、可视化区域裁剪等相关内容。试验表明:全球格网动态生成的效率110～370单元/ms之间,加载空间数据后,格网数据和空间数据逐层加载的时间在300 ms左右,能够保证加载空间数据后的显示刷新率在20帧/s左右。     

探讨3维地物模型制作过程

自第一台计算机诞生以来,计算机技术迅速发展,并在各行各业中得到广泛应用。3维显示技术是随着计算机软硬件的快速发展而兴起的一个新的应用方向。它能在计算机中为用户提供一个3维的直观环境,通过视觉提高真实感。随着地理信息产业的快速发展,人们对3维地理空间支撑的需求越来越强烈,"数字城市"项目在各大城市如火如荼的发展,3维GIS进入了高速发展和广泛应用的新时期。这里探讨了综合运用3dsMax,Arc Map,creator,CAD,PhotoShop等软件制作3维地物模型的过程。     

A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.     

Cycle graph analysis for 3D roof structure modelling: Concepts and performance

The paper presents a cycle graph analysis approach to the automatic reconstruction of 3D roof models from airborne laser scanner data. The nature of convergences of topological relations of plane adjacencies, allowing for the reconstruction of roof corner geometries with preserved topology, can be derived from cycles in roof topology graphs. The topology between roof adjacencies is defined in terms of ridge-lines and step-edges. In the proposed method, the input point cloud is first segmented and roof topology is derived while extracting roof planes from identified non-terrain segments. Orientation and placement regularities are applied on weakly defined edges using a piecewise regularization approach prior to the reconstruction, which assists in preserving symmetries in building geometry. Roof corners are geometrically modelled using the shortest closed cycles and the outermost cycle derived from roof topology graph in which external target graphs are no longer required. Based on test results, we show that the proposed approach can handle complexities with nearly 90% of the detected roof faces reconstructed correctly. The approach allows complex height jumps and various types of building roofs to be firmly reconstructed without prior knowledge of primitive building types.