Developing a geospatial web-GIS system for landscape and urban planning

This study discusses a geographical information system (GIS) for operating a local government's landscape and urban planning activities via a website. Implementing this web-GIS system will help build a more realistic landscape and urban planning model that includes citizen participation and city marketing. The approach is applicable to ubiquitous city (u-city) development based on geospatial web and its related systems. The approach presented is built on six selected elements of a u-city system. The outcome of the study includes sustainable analysis, environmental planning, urban planning, and city marketing. The outcome is applicable to cities that are planning to adopt the u-city system or advanced telecommunication or planning tools into their urban frameworks.     

Towards intelligent geospatial data discovery: a machine learning framework for search ranking

Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset. This study reports a machine learning framework to address the ranking challenge, the fundamental obstacle in geospatial data discovery, by (1) identifying a number of ranking features of geospatial data to represent users’ multidimensional preferences by considering semantics, user behavior, spatial similarity, and static dataset metadata attributes; (2) applying a machine learning method to automatically learn a ranking function; and (3) proposing a system architecture to combine existing search-oriented open source software, semantic knowledge base, ranking feature extraction, and machine learning algorithm. Results show that the machine learning approach outperforms other methods, in terms of both precision at K and normalized discounted cumulative gain. As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain, we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.     

Defining a framework for integration of geospatial technologies for emergency management

Geospatial technologies are emerging as one of the most promising environments for development of emergency management framework. Geospatial technologies such as Remote Sensing, Geographic Information Systems and Global Positioning Systems integrated with valuable near/real-time field information can provide a comprehensive platform for emergency management. Satellite imagery, vector database, Location Based Services and ancillary data having the relevant attribute information with access and query tools helps the disaster manager to analyse the required information for relief operations. However, the conceptual design of the database, integration procedures, development of decision support functions and dissemination of information integrating real-time information from the field are very important issues in development framework for emergency management. There are some technological challenges in the emergency response for deriving the required outputs such as damage assessment, evacuation planning, situation monitoring, information dissemination, and coordination of resource and logistic planning. This article provides a framework for the integration of various multi-disciplinary technologies for addressing emergency management.     

Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continuous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continuous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzziness significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN(triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.     

Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzzines significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN (triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.     

A site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data

The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, we propose a multi-scene micro-scale urban substation siting framework (UrbanPS): (1) The framework uses multi-source big data and the machine learning model to estimate fine-scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. We also found that the power can be saved by dynamic regulation of substation operation.     

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

Abstract

While significant progress has been made to implement the Digital Earth vision, current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes. To achieve effectiveness of decision-making using Digital Earth for understanding the Earth and its systems, new infrastructures that provide capabilities of computational simulation are needed. This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems (SDSSs) to expand capabilities of the currently implemented infrastructure of Digital Earth. Main technologies applied in the framework such as heterogeneous ontology integration, ontology-based catalog service, and web service composition were introduced. We proposed a partition-refinement algorithm for ontology matching and integration, and an algorithm for web service discovery and composition. The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet. Based on the proposed framework, a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.     

Towards a collaborative global land cover information service

ABSTRACT

The need and critical importance of global land cover and change information has been well recognized. Although rich collection of such information has been made available, the lack of necessary information services to support its easy access, analysis and validation makes it difficult to find, evaluate, select and reuse them through well-designed workflows. Aiming at promoting the development of the needed global land cover information services, this paper presents a conceptual framework for developing a Collaborative Global Land Cover Information Service (CoGland), followed by discussions on its implementation strategies. The framework supports connected and shared land cover and change web services around the world to address resource sharing, community service and cross-board collaboration needs. CoGland can benefit several recent international initiatives such as Future Earth, and many societal benefit areas. The paper further proposes that CoGland be developed within the framework of the Group on Earth Observations with the support of a number of key organizations such as the United Nations Expert Committee on Global Geospatial Information Management, the International Society for Photogrammetry and Remote Sensing, and International Society of Digital Earth. It is hoped that this paper can serve as a starting point for further discussions on CoGland developments.     

The next layer: Towards open pedagogy in geospatial education

Open-source software and open data are becoming increasingly popular in the teaching and learning of geographic information science. The cost savings that are derived from using free software over proprietary software are one driving factor, yet the move from “closed” to “open” represents much more than financial austerity—it signifies a broader shift in educational philosophy. This article documents the gradual transition of an introductory undergraduate course in geographic information systems from an entirely closed course to one that has become increasingly open. Having completely adopted the first three layers of open—software, data, and educational resources—the course is now turning toward the next layer: embracing the philosophy of open pedagogy.     

展望5G/6G时代的地球空间信息技术

随着通信技术的发展,5G/6G时代逐渐到来。在新的网络环境下,地球空间信息技术的发展也将催生新的发展趋势。文章首先对5G/6G时代进行了论述,分析其主要特点。然后阐述了5G/6G时代下地球空间信息技术的发展趋势(真三维实景模型的形成,地球空间信息处理的智能化和自动化,地球空间信息服务的社会化和大众化)。最后分析了新时代背景下我国自主的通导遥一体化空天信息实时智能服务系统建设的必要性;分析其发展路线(局域服务系统,区域服务系统,全球服务系统)和技术储备;对5G/6G、大数据和人工智能技术支撑下,我国地球空间信息技术的发展进行了展望。     

城市地理空间数据粒度的比较与确定方法研究

本文以空间数据多边形的平均等面积圆直径作为粒度特征尺度,将城市地理空间数据分成六个关键空间层次,并对各空间层次数据进行了粒度分析,建立了空间粒度与空间层次的数学模型。基于本研究确立的关键空间层次,以城市问题的研究精度作为选择条件,讨论了按粒度等级选择适当空间层次的方法。同时详细描述了利用GIS软件提取、制作各空间层次数据的具体技术步骤和方法。     

面向智能化测绘的城市地物三维提取

精准有效的三维时空信息是新型基础设施建设、实景三维中国、自然资源管理与监测等国家重大需求不可或缺的重要支撑。测绘装备的快速发展提升了点云和影像获取的便捷性,为智能化测绘提供了一种全新技术手段。然而,如何从点云、影像中智能地提取三维时空信息仍然面临诸多困境。本文围绕城市场景中地物目标智能化提取的需求,重点阐述点云位置精度改善、点云与全景影像融合、部件级典型地物要素精细提取、软件研发与工程实践4个方面的关键技术与研究进展,为实景三维中国建设等提供关键支持。     

CPU+GPU异构环境下数据密集型矢量多边形地理大数据并行框架

本文提出了面向CPU+GPU异构环境的数据密集型矢量多边形地理大数据并行计算框架(PFGAP)。PFGAP将数据密集型矢量多边形地理大数据的并行计算分解为算子、数据、粒度、并行环境及任务调度5个模块,分别设计相应的负载均衡并行计算策略;通过封装并行计算实现细节及数据密集型多边形算子的快速并行化。试验采用多边形三角剖分、栅格化及投影变换作为测试算例,采用土地利用数据作为测试数据,在不同类型的并行环境中计算并行效率。结果表明,PFGAP能很好地适用于不同类型的数据集、算子及并行计算环境。利用PFGAP实现的并行算法显著地降低了串行执行时间,取得了40.03的最优并行加速比。试验还分别测试了各个模块涉及的并行策略,结果表明取得的并行效率优于现有并行策略。     

地理信息公共服务平台数据更新技术——以数字沈阳地理空间框架为例

结合沈阳市地理信息公共服务平台数据更新项目,研究地理共享平台数据的更新方式、更新技术以及更新发布一体化流程等关键技术。采用增量更新技术矢量数据,基于历史要素与现势要素节点、分线段的位置关系,检测并提取数据库中更新的要素;利用分词检测方法检测地名信息的匹配度,进行地名数据的更新;基于地名与路网数据,对公交数据进行检测、采集、优化等流程,完成公交数据的更新;并建立平台数据更新与发布一体化流程。经过作业流程控制与编写辅助程序,极大提高数据更新的速度与质量控制水平。     

城市规划CA模型在城市空间形态演化中的应用研究

传统的CA模型基于自下而上的思路模拟城市复杂系统,忽略了宏观决策对城市空间形态演化的影响。本文基于传统CA模型,从城市规划角度,通过引入控制性详细规划层,把整个城市空间划分成不同组团片区,将传统的自下而上CA模型改进为自下而上和自上而下相结合的城市规划CA模型(UPCA),并取得了较好的实证模拟结果。结果表明:该模型简化了传统CA城市模拟模型的数据量,提高了模型的模拟精度,并增强了模型的规划决策功能,使模型具备了一定的社会经济分析能力。     

Modelling relational contexts in GEOBIA framework for improving urban land-cover mapping

In human cognition, both visual features (i.e., spectrum, geometry and texture) and relational contexts (i.e. spatial relations) are used to interpret very-high-resolution (VHR) images. However, most existing classification methods only consider visual features, thus classification performances are susceptible to the confusion of visual features and the complexity of geographic objects in VHR images. On the contrary, relational contexts between geographic objects are some kinds of spatial knowledge, thus they can help to correct initial classification errors in a classification post-processing. This study presents the models for formalizing relational contexts, including relative relations (like alongness, betweeness, among, and surrounding), direction relation (azimuth) and their combination. The formalized relational contexts were further used to define locally contextual regions to identify those objects that should be reclassified in a post-classification process and to improve the results of an initial classification. The experimental results demonstrate that the relational contexts can significantly improve the accuracies of buildings, water, trees, roads, other surfaces and shadows. The relational contexts as well as their combinations can be regarded as a contribution to post-processing classification techniques in GEOBIA framework, and help to recognize image objects that cannot be distinguished in an initial classification.     

GIS在城市规划中的应用

本文概述了地理信息系统的概念及城市规划的主要内容,提出了城市规划与GIS相结合的城市规划地理信息系统并阐述了城市规划地理信息系统的实现及功能,提高了城市规划的科学性和合理性。