Mapping Flood Exposure, Damage, and Population Needs Using Remote and Social Sensing: A Case Study of 2022 Pakistan Floods

Zainab Akhtar, Umair Qazi, Rizwan Sadiq, Aya El-Sakka, Muhammad Sajjad, Ferda Ofli, Muhammad Imran

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)

Abstract

The devastating 2022 floods in Pakistan resulted in a catastrophe impacting millions of people and destroying thousands of homes. While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. Our satellite-based analysis over a one-month period (25 Aug-25 Sep) revealed that 11.48% of Pakistan was inundated. When combined with geospatial data, this meant 18.9 million people were at risk across 160 districts in Pakistan, with adults constituting 50% of the exposed population. Our social sensing data analysis surfaced 106.7k reports pertaining to deaths, injuries, and concerns of the affected people. To understand the urgent needs of the affected population, we analyzed tweet texts and found that South Karachi, Chitral and North Waziristan required the most basic necessities like food and shelter. Further analysis of tweet images revealed that Lasbela, Rajanpur, and Jhal Magsi had the highest damage reports normalized by their population. These damage reports were found to correlate strongly with affected people reports and need reports, achieving an R-Square of 0.96 and 0.94, respectively. Our extensive study shows that combining remote sensing, social sensing, and geospatial data can provide accurate and timely information during a disaster event, which is crucial in prioritizing areas for immediate and gradual response.

Original languageEnglish
Title of host publicationWWW '23
Subtitle of host publicationProceedings of the ACM Web Conference 2023
EditorsYing Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages4120-4128
Number of pages9
Edition1st
ISBN (Electronic)9781450394161
DOIs
Publication statusPublished - Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - AT&T Hotel and Conference Center at The University of Texas at Austin, Austin, United States
Duration: 30 Apr 20234 May 2023
https://www2023.thewebconf.org/program/detailed-program/
https://dl.acm.org/doi/proceedings/10.1145/3543507

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • crisis response
  • image processing
  • remote sensing
  • social sensing
  • text processing

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