Investigations of disaster information representation from a geospatial perspective: Progress, challenges and recommendations

Li, Weilian; Zhu, Jun; Pirasteh, Saied; Zhu, Qing; Fu, Lin; Wu, Jianlin; Hu, Yan; Dehbi, Youness

doi:10.1111/tgis.12922

Cited by 32 publications

(11 citation statements)

References 118 publications

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“…(Karimiziarani et al, 2022; Kavota et al, 2020; Mavrodieva & Shaw, 2021; Raza et al, 2020; Shiau et al, 2018). However, using social media as information sources for disaster management is highly problematic for reasons including rumors, misinformation, duplicated information, and inaccurate location (Li, Zhu, Pirasteh, et al, 2022; Martí et al, 2019; Neppalli et al, 2017). Still, researchers are optimistic about the value of using social media data in disaster management and crisis communication (Hughes & Palen, 2012; Neppalli et al, 2017).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Over the past few decades, extreme natural hazards have become more common as a consequence of climate change, which has posed severe challenges to disaster prevention and mitigation (Bhatt et al, 2015; Li, Zhu, Pirasteh, et al, 2022; Li et al, 2020; Ryan et al, 2020). Among them, floods are one of the most widespread natural disasters on Earth (Hong et al, 2007; Li et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Li,

Haunert,

Knechtel

et al. 2023

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Detecting and collecting public opinion via social media can provide near real‐time information to decision‐makers, which plays a vital role in urban disaster management and sustainable development. However, there has been little work focusing on identifying the perception and the sentiment polarity expressed by users during and after disasters, particularly regional flood events. In this article, we comprehensively analyze tweets data related to the “European floods in 2021” over time, topic, and sentiment, forming a complete workflow from data processing, topic modeling, sentiment analysis, and topic and sentiment prediction. The aim is to address the following research questions: (1) What are the public perception and main concerns during and after floods? (2) How does the public sentiment change during and after floods? Results indicate that there is a significant correlation between a flood's trend and the heat of corresponding tweets. The three topics that receive the most public concern are: (1) climate change and global warming; (2) praying for the victims: and (3) disaster situations and information. Negative sentiments are predominant during the floods and will continue for some time. We tested five different classifiers, of which TextCNN‐attention turned out to deliver the best predictions in topic and sentiment prediction, and performed well for sparse flood tweets, it can be used to predict the topic and sentiment polarity of a single tweet in real‐time during the flood events. Our findings can help disaster agencies to better understand the dynamics of social networks and develop stronger situational awareness towards a disaster, which can contribute to scientifically justified decision‐making in urban risk management and also meet the challenges associated with the global sustainable development goal 11 (SDGs) on Sustainable Cities and Communities.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Li,

Haunert,

Knechtel

et al. 2023

Transactions in GIS

Self Cite

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“…The current methods for constructing knowledge graphs can be broadly classified into top‐down and bottom‐up approaches. The top‐down approach initially identifies the subject domain and specific requirements, then collects pertinent concepts and relationships to design a conceptual model (Li et al., 2022). For instance, this could entail creating an ontology as a schema layer and subsequently instantiating the identified concepts and relationships to construct a knowledge graph (Zhang et al., 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

Zhang,

Zhao,

Han

et al. 2023

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China's railway construction is rapidly transitioning toward integrated management of “stakeholders, management elements, and management processes”. Therefore, comprehensive and whole‐process digital twin scene modeling is urgently needed for intelligent railway construction. However, the requirements of three‐dimensional scenes in different stages vary hierarchically, resulting in a lack of construction semantics and limited universality in modeling. This article proposes a knowledge‐guided digital twin modeling method of hierarchical scenes for a high‐speed railway. We first build a knowledge graph of “knowledge‐model‐data” to achieve an accurate and hierarchical description of railway scenes. We then establish a parameter‐driven modeling method that integrates knowledge guidance and primitive combination to generate a display scene and a virtual design scene automatically. Third, we propose joint linkage and model growth methods for construction action modeling, which are used to generate a virtual construction scene. Finally, in response to the hierarchical scene‐generating requirements in different stages, we conduct intelligent modeling experiments for the entire design and construction process. The knowledge graph of the hierarchical semantic description mode significantly improves the flexibility and universality of the modeling method. The proposed modeling method for the entire process contributes to the rapid representation of design data, in‐depth design, visual exploration, and dynamic optimization of the construction process. This article provides a reliable digital twin modeling solution for the entire process to improve design and construction quality.

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“…In summary, it is essential to develop an accurate, quick, and data-efficient dynamic evolution approach for landslides [22][23][24][25][26][27][28]. On the one hand, the multi-dimensional synthesis dynamically expresses the changes in spatial position, geometric form, and attribute information of geographical objects in the time dimension.…”

Section: Landslide Visualizationmentioning

confidence: 99%

Reconstruction and Visualization of Landslide Events Based on Pre- and Post-Disaster Remote Sensing Data

Luo

Yang

Huang

et al. 2023

Water

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This paper proposes a method to reconstruct and visualize landslide events based on pre- and post-disaster remote sensing data. The proposed method establishes the dynamic equations of the landslide evolution process and calibrates the model parameters based on pre- and post-disaster remote sensing data. Based on the calibrated dynamic equations, we reconstruct and simulate the historical landslide process and visualize the landslide evolution. The experimental results show that our method could dynamically and realistically reconstruct and visualize the landslide evolution process. Moreover, the landslide process simulation can also detect the maximum depth, maximum sliding speed, maximum momentum, and other indicators during the evolution process, and the visualization results can be used for subsequent hazard assessment, engineering implementation, and other applications.

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Investigations of disaster information representation from a geospatial perspective: Progress, challenges and recommendations

Cited by 32 publications

References 118 publications

Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Knowledge‐guided digital twin modeling method of generating hierarchical scenes for a high‐speed railway

Reconstruction and Visualization of Landslide Events Based on Pre- and Post-Disaster Remote Sensing Data

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