面向GIS专业的计算机图形学课程教学探讨

计算机图形学作为GIS专业基础课之一,对学生专业能力构成起夯实基础和促进应用的作用。文中在分析计算机图形学教学特点的基础上,结合自身实践,探讨基于以内容结合为导向,面向实践的GIS专业计算机图形学教学改革,并在实践中取得较好的教学效果。     

黑龙江省位置服务中心建设与发展

重点介绍黑龙江省位置服务中心十年建设过程与研究成果,就中心硬件配备、平台建设进行阐述;对位置服务平台的逻辑组成、功能架构、终端产品接入及服务接口情况作详细说明;针对中心在北斗领域的研究应用、多年来探索的服务模式,以及位置服务标准化建设所做工作进行探讨。文末对黑龙江省位置服务中心下一步的新技术研究与产品化、市场化策略予以展望。     

多核并行环境中矢量数据格式的转换算法

针对目前对VCT格式转换的并行算法研究较少的现状,该文以VCT向shapefile格式转换为例,探索多核并行环境中进行VCT格式向其他矢量数据格式快速转换的方法:在深入探索VCT与shapefile格式特点的基础上,通过构建索引、数据解析转换,完成由VCT向shapefile格式的转换;并设计出一种顾及负载均衡的数据划分方法,通过评估不同类型图层的复杂度实现不同类型矢量数据的均衡划分,实现了VCT向shapefile格式转换的并行算法。最后利用某地土地利用VCT文件进行算法测试,结果表明该算法转换结果正确,取得了良好的加速比,并基本达到负载均衡。     

地理信息系统精品课程共享资源建设

针对如何提高地理信息系统课程教育的教学质量问题,该文分析了GIS精品课程共享资源持续建设的情况,阐述了课程模块、知识点、教学内容及理论教学与实践教学如何融合,重点介绍了创新型课程团队、立体式教材体系及开放式网络课程等方面建设情况。研究成果可以为进一步推动GIS教学改革提供参考。     

地理实体的数据处理技术在地理信息数据库建设中的应用

介绍了地理实体的概念和基本地理实体的内容,并对地理实体在地理信息数据库建设过程中的数据抽取、格式转换、数据装载等数据标准化的主要数据处理技术方法进行了陈述,为其它相关地理信息数据库建设项目提供基本方法参考。     

素质教育背景下地理教师的GIS素质构建

在对地理教师地理科学素质现状分析的基础上,对地理教师在教学、科研、生活中应具备的地理信息科学素质进行了研究,并从师范教育、继续教育2个角度探讨了构建地理教师地理信息科学素质的方法。     

GIS技术在煤田地质信息资源管理领域应用探讨

提出了GIS技术应用服务于煤田地质信息资源管理领域的背景,以煤田地质信息资源管理相关部门应用需求为基础,提出了基于GIS技术的煤田地质信息资源管理地理信息服务系统架构、网络运行环境和硬件结构、数据库、功能等设计。最后结合煤田地质信息资源管理地理信息服务试验系统设计与开发,展示了试验系统特色部分开发内容。     

重庆市住房保障信息系统建设及应用

本文在对重庆市住房保障管理业务进行分析的基础上,结合实际工作,以廉租住房和公共租赁住房为例,就如何建立一体化的住房保障信息系统进行了介绍,重点对该系统如何确保保障性住房公平、公开、透明的分配,如何提高保障性房源的高效利用进行了介绍。     

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.