Online emergency mapping based on disaster scenario and data integration

F. Ren, Yiwen Li, Zhihe Zheng, Han Yan, Qingyun Du
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引用次数: 0

Abstract

ABSTRACT Emergency mapping is a task that requires abundant professional knowledge and complex operations, especially in high timeliness demands in disaster scenarios. The operation of the conventional cartographic system based on GIS in the desktop environment is complicated and requires operators to have strong professional skills, while the network environment provides low-cost, unified geographical data services and easy-to-use operation without using any specific software. In this paper, we propose an online emergency mapping framework, which is a novel idea for rapid mapping and combining multi-source heterogeneous disaster data with cartographer knowledge. Through a disaster scenario model for emergency mapping, the corresponding relationship between scenarios and map groups can be understood. Through disaster data integration, knowledge rules, mapping templates, map symbol engines, and a simple wizard, an emergency response map can be rapidly produced. With the support of the techniques mentioned above, a prototype system is developed to verify the efficiency and validity of the framework. The results demonstrate that a framework that can effectively assist decision makers in displaying the present situation clearly and accurately has high practical value. The study also provides a novel perspective for shortening the mapping cycle in emergencies.
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基于灾害场景和数据集成的在线应急映射
应急制图是一项专业知识丰富、操作复杂的任务,特别是在灾害场景下对时效性要求较高。传统的基于GIS的制图系统在桌面环境下操作复杂,要求操作人员具有较强的专业技能,而网络环境提供了低成本、统一的地理数据服务,无需使用任何特定的软件即可方便操作。本文提出了一种在线应急制图框架,为快速制图和将多源异构灾害数据与制图员知识相结合提供了一种新思路。通过应急映射的灾难场景模型,可以了解场景与映射组之间的对应关系。通过灾害数据集成、知识规则、映射模板、地图符号引擎和简单的向导,可以快速生成应急响应地图。在上述技术的支持下,开发了一个原型系统来验证该框架的效率和有效性。结果表明,一个能够有效帮助决策者清晰、准确地展示现状的框架具有很高的实用价值。该研究还为缩短紧急情况下的测绘周期提供了新的视角。
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来源期刊
CiteScore
5.00
自引率
0.00%
发文量
10
期刊介绍: International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
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