Karst database development in Minnesota: design and data assembly

The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.     

从平台GIS到跨平台互操作GIS的发展

介绍了跨平台互操作GIS的基本概念、主要关键技术和目前的发展状况以及该技术在我国的进展。     

基础地理信息应用服务研究

本文全面、系统地分析了基础地理信息在地理信息产业中的作用、基础地理信息的各种应用，并在此基础上提出了基础地理信息的服务策略。     

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。     

基于Web服务移动拼车系统的设计与实现

介绍了一种利用手机应用程序的实时性和Web应用程序的广泛性、便利性开发系统程序,共同提供服务的拼车系统。该系统以Web服务为基础,实现了拼车数据在Web端和手机端的实时共享,使拼车更加便捷,提倡了低碳、环保、节能的生活理念。     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

基于要素服务的地图在线编辑系统设计与实现

针对空间信息平台用户对空间数据的上传、下载、发布和在线编辑的需求,设计实现了基于ArcGIS Server要素服务和Flex的具有地图数据在线编辑功能的Web GIS系统。本系统应用RIA技术,实现了空间与属性数据的在线编辑,符号在线编辑和用户数据管理等,用户体验上也得到很大提升。通过工程化的设计实现,能够满足＂平台＂跨部门多应用主体的实际需求。     

基于RIA和SOA的分布式WebGIS研究

基于当前流行的RIA平台和SOA体系,以分布式WebGIS为研究对象,探讨RIA/Services架构的分布式WebGIS开发方式,并以Silverlight和ArcGIS Server为平台阐述实践方案,开发的实验系统提高了WebGIS的表现力与交互性,同时降低了开发的复杂性、缩短了产品生产周期,具有一定的理论和现实意义。     

空间信息服务模式研究

通过分析空间信息服务中的典型空间信息流动增值过程,建立了三种空间信息服务模式:线性传递的链状模式、共建共享的星状模式、基于Web2.0的网状模式.分析了每种模式的空间信息流模型与增值过程,并讨论了适合的空间信息服务应用,以及在Internet上未来会形成的空问信息增值服务网络,展望了空间信息服务的大众化发展方向.结合应用案例,分析了网状服务模式在网格GIS中的应用.