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 language | English |
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Title of host publication | WWW '23 |
Subtitle of host publication | Proceedings of the ACM Web Conference 2023 |
Editors | Ying Ding, Jie Tang, Juan Sequeda, Lora Aroyo, Carlos Castillo, Geert-Jan Houben |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 4120-4128 |
Number of pages | 9 |
Edition | 1st |
ISBN (Electronic) | 9781450394161 |
DOIs | |
Publication status | Published - Apr 2023 |
Event | 2023 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 2023 → 4 May 2023 https://www2023.thewebconf.org/program/detailed-program/ https://dl.acm.org/doi/proceedings/10.1145/3543507 |
Conference
Conference | 2023 World Wide Web Conference, WWW 2023 |
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Country/Territory | United States |
City | Austin |
Period | 30/04/23 → 4/05/23 |
Internet address |
Scopus Subject Areas
- Computer Networks and Communications
- Software
User-Defined Keywords
- crisis response
- image processing
- remote sensing
- social sensing
- text processing