Utilizing Volunteered Geographic Information for Real-Time Analysis of Fire Hazards: Investigating the Potential of Twitter Data in Assessing the Impacted Areas

Author:

Florath Janine12ORCID,Chanussot Jocelyn2ORCID,Keller Sina1ORCID

Affiliation:

1. Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

2. GIPSA-Lab, Université Grenoble Alpes, CNRS, Grenoble INP, 38402 Saint Martin d’Heres, France

Abstract

Natural hazards such as wildfires have proven to be more frequent in recent years, and to minimize losses and activate emergency response, it is necessary to estimate their impact quickly and consequently identify the most affected areas. Volunteered geographic information (VGI) data, particularly from the social media platform Twitter, now X, are emerging as an accessible and near-real-time geoinformation data source about natural hazards. Our study seeks to analyze and evaluate the feasibility and limitations of using tweets in our proposed method for fire area assessment in near-real time. The methodology involves weighted barycenter calculation from tweet locations and estimating the affected area through various approaches based on data within tweet texts, including viewing angle to the fire, road segment blocking information, and distance to fire information. Case study scenarios are examined, revealing that the estimated areas align closely with fire hazard areas compared to remote sensing (RS) estimated fire areas, used as pseudo-references. The approach demonstrates reasonable accuracy with estimation areas differing by distances of 2 to 6 km between VGI and pseudo-reference centers and barycenters differing by distances of 5 km on average from pseudo-reference centers. Thus, geospatial analysis on VGI, mainly from Twitter, allows for a rapid and approximate assessment of affected areas. This capability enables emergency responders to coordinate operations and allocate resources efficiently during natural hazards.

Funder

AXA Research Fund

MIAI@Grenoble Alpes

Publisher

MDPI AG

Subject

Earth and Planetary Sciences (miscellaneous),Safety Research,Environmental Science (miscellaneous),Safety, Risk, Reliability and Quality,Building and Construction,Forestry

Reference48 articles.

1. The relationship between natural disaster and economic development: A panel data analysis;Songwathana;Procedia Eng.,2018

2. Wisner, B., Blaikie, P., Cannon, T., and Davis, I. (2014). At Risk: Natural Hazards, People’s Vulnerability and Disasters, Routledge.

3. Leveraging multimodal social media data for rapid disaster damage assessment;Hao;Int. J. Disaster Risk Reduct.,2020

4. Florath, J., and Keller, S. (2022). Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area. Remote Sens., 14.

5. Dittrich, A., and Lucas, C. (2014). Connecting a Digital Europe through Location and Place, Proceedings of the AGILE’2014 International Conference on Geographic Information Science, Castellon, Spain, 3–6 June 2014, AGILE Digital Editions.

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