no code implementations • 14 Feb 2022 • Ferda Ofli, Umair Qazi, Muhammad Imran, Julien Roch, Catherine Pennington, Vanessa Banks, Remy Bossu
This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques.
1 code implementation • 4 Oct 2021 • Muhammad Imran, Umair Qazi, Ferda Ofli
The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports.
no code implementations • 3 Oct 2021 • Ferda Ofli, Muhammad Imran, Umair Qazi, Julien Roch, Catherine Pennington, Vanessa J. Banks, Remy Bossu
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly.
no code implementations • 7 Apr 2021 • Firoj Alam, Umair Qazi, Muhammad Imran, Ferda Ofli
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
no code implementations • 17 Nov 2020 • Firoj Alam, Ferda Ofli, Muhammad Imran, Tanvirul Alam, Umair Qazi
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
1 code implementation • 22 May 2020 • Umair Qazi, Muhammad Imran, Ferda Ofli
The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters.
no code implementations • 14 Apr 2020 • Muhammad Imran, Firoj Alam, Umair Qazi, Steve Peterson, Ferda Ofli
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings.