An augmented representation method of debris flow scenes to improve public perception

ABSTRACT

Virtual scenes can present rich and clear disaster information, which can significantly improve the level of public disaster perception. However, existing methods for constructing scenes of debris flow disasters have some deficiencies. First, the construction process does not consider public knowledge, which makes it difficult for the constructed scenes to meet the requirements of the public. Second, the scene representation emphasizes visual effects but lacks augmented visualization, leading to scarcity of semantic information and inefficient public perception. In this paper, the optimal selection of scene objects, semantic augmentation through the combination of various visual variables and dynamic augmented representation are discussed in detail. Finally, a debris flow that occurred Shuimo town is selected for experiment analysis. The experimental results show that most people are unaware of the risks posed by debris flow disasters. The public is more concerned about the consequences of a disaster than its spatiotemporal process, especially when the consequences are related to their own interests. Furthermore, an augmented representation can increase the amount of semantic information of scene objects, which is essential for enhancing public understanding of the causes, processes and effects of debris flows and thereby changing people’s attitudes and enhancing their risk perception.

KEYWORDS:

• Debris flow disaster
• public perception
• virtual geographical scene
• visual variable combination
• augmented representation

Supplementary material

Supplemental data for this article can be accessed here.

Data and code availability statement

The data and codes that support the findings of this study are available at figshare.com under the identifier https://doi.org/10.6084/m9.figshare.13040702.v1.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This paper was supported by the National Natural Science Foundation of China (Grant Nos. 41941019 and 41871289), the Sichuan Youth Science and Technology Innovation Team (Grant No. 2020JDTD0003), the Fundamental Research Funds for the Central Universities (Grant No. 2682018CX35), and the Doctoral Innovation Fund Program of Southwest Jiaotong University.

Notes on contributors

Weilian Li

Weilian Li received the B.S. degree in survey engineering from Tianjin Chengjian University, Tianjin, China, in 2015. He is currently working toward the Ph.D. degree at Southwest Jiaotong University, Chengdu, China. His research interests include virtual geographic environments and disaster scene visualization.

Jun Zhu

Jun Zhu received the M.S. degree in geodesy and survey engineering from Southwest Jiaotong University, Chengdu, China, in 2003, and the Ph.D. degree in cartography and geographic information systems from the Chinese Academy of Sciences, Beijing, China, in 2006. From 2007 to 2008, he was a Postdoctoral Research Fellow with the Chinese University of Hong Kong, Shatin, Hong Kong. Currently, he is a Professor with the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University. His research interests include computer vision, 3-D GIS technology, and virtual geographic environments.

Lin Fu

Lin Fu received the B.S. degree in geographic information science from the School of Land and Resources, China West Normal University, NanChong, China, in 2017. He is currently working toward the Ph.D. degree at Southwest Jiaotong University, Chengdu, China. His research interests include 3-D GIS technology, deep learning, and virtual geographic environments.

Qing Zhu

Qing Zhu received the Ph.D. degree in railway engineering from North Jiaotong University, Beijing, China, in 1995. From 1997, he had worked as a Professor with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China, and received the Cheung Kong Scholars, in 2009. Since 2014, he has been a Professor and the Director of Research Committee with the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. He also serves as Visiting Professor for the Wuhan University, Central South University, and Chongqing University. He has authored or coauthored more than 260 articles and seven books. His research interests include photogrammetry, geographic information system, and virtual geographic environment. Dr. Zhu is the Editor-in-Chief for the Journal of Smart Cities and also serves as Editorial Board members of more than ten journals including Computers, Environment and Urban Systems, Transactions in GIS, and International Journal of 3D Information.

Yakun Xie

Yakun Xie received the B.S. degree in survey engineering from the School of Survey Engineering, Henan University of Urban Construction, Pingdingshan, China, in 2015, and the M.S. degree in geodesy and survey engineering from the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China, in 2018, where he is currently working toward the Ph.D. degree. His research interests include intelligent fire control, computer vision, and remote sensing image processing.

Ya Hu

Ya Hu received the M.S. degree in geodesy and survey engineering from Southwest Jiaotong University, Chengdu, China, in 2005. From 2008 to 2010, he was a Research Assistant with the Chinese University of Hong Kong, Shatin, Hong Kong. Currently, he is a lecturer with the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University. His research interests 3-D GIS technology and virtual geographic environments.