An efficient flood dynamic visualization approach based on 3D printing and augmented reality. Issue 11 (1st November 2020)

• Record Type: Journal Article
• Authors: Zhang, Guoyong; Gong, Jianhua; Li, Yi; Sun, Jun; Xu, Bingli; Zhang, Dong; Zhou, Jieping; Guo, Ling; Shen, Shen; Yin, Bingxiao
• Is Part Of: International journal of digital earth. Volume 13:Issue 11(2020)
• Page Start: 1302
• Page End: 1320
• Publication Date: 2020-11-01
• Subjects: Flood visualization -- augmented reality -- 3D-printed terrain -- virtual geographic environment -- augmented reality geovisualization; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2019.1711210
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Flood visualization is an effective and intuitive tool for representing flood information from abstract spatiotemporal data. With the growing demand for flood disaster visualizations and mitigation, augmented flood visualizations that support decision makers' perspectives are needed, which can be enhanced by emerging augmented reality (AR) and 3D printing technologies. This paper proposes an innovative flood AR visualization method based on a 3D-printed terrain model and investigates essential techniques, such as the suitable size calculation of the terrain models, the adaptive processing of flood data, and hybridizing virtual flood and terrain models. A prototype experimental system (PES) based on the proposed method and a comparison experimental system (CES) based on a virtual terrain are developed to conduct comparative experiments, which combine the system performance and questionnaire method to evaluate the efficiency and usability of the proposed method. The statistical results indicate that the method is useful for assisting participants in understanding the flood hazard and providing a more intuitive and realistic visual experience compared with that of the traditional AR flood visualization method. The frame rate is stable at 60 frames per second (FPS), which means that the proposed method is more efficient than the traditional AR flood visualization method.