F. Ren, Yiwen Li, Zhihe Zheng, Han Yan, Qingyun Du
{"title":"Online emergency mapping based on disaster scenario and data integration","authors":"F. Ren, Yiwen Li, Zhihe Zheng, Han Yan, Qingyun Du","doi":"10.1080/19479832.2021.1963329","DOIUrl":null,"url":null,"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.","PeriodicalId":46012,"journal":{"name":"International Journal of Image and Data Fusion","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Image and Data Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/19479832.2021.1963329","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
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.
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.).