102 citations · 350 across the 24 of their papers we have counts for
24 papers
AI-Driven Disaster Response and Displacement Monitoring
Noora Al-Emadi, Muhammad Imran, Yin Yang +5
ARTIFICIAL INTELLIGENCE (AI) has demonstrated its potential across a wide range of humanitarian tasks, offering scalable and data-driven solutions to some of the world's most press…
DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
Aisha Al-Mohannadi, Ayisha Firoz, Yin Yang +2
Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage assessment and response. While Visua…
GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response
Ahmed El Fekih Zguir, Ferda Ofli, Muhammad Imran
LLMs excel at linguistic tasks but lack the inner geospatial capabilities needed for time-critical disaster response, where reasoning about road networks, coordinates, and access t…
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Noora Al-Emadi, Ingmar Weber, Yin Yang +1
Detecting vehicles in satellite images is crucial for traffic management, urban planning, and disaster response. However, current models struggle with real-world diversity, particu…
Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs
Ahmed El Fekih Zguir, Ferda Ofli, Muhammad Imran
Natural disasters often result in a surge of social media activity, including requests for assistance, offers of help, sentiments, and general updates. To enable humanitarian organ…
Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery
Sara Al-Emadi, Yin Yang, Ferda Ofli
Object detectors have achieved remarkable performance in many applications; however, these deep learning models are typically designed under the i.i.d. assumption, meaning they are…
Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features
Muhammad Imran, Abdul Wahab Ziaullah, Kai Chen +1
The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data f…
Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
Abdul Wahab Ziaullah, Ferda Ofli, Muhammad Imran
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies…
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt +6
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countr…
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
Fevziye Irem Eyiokur, Alperen Kantarcı, Mustafa Ekrem Erakın +8
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced ar…
Bias-Aware Face Mask Detection Dataset
Alperen Kantarcı, Ferda Ofli, Muhammad Imran +1
In December 2019, a novel coronavirus (COVID-19) spread so quickly around the world that many countries had to set mandatory face mask rules in public areas to reduce the transmiss…
Learning Program Representations for Food Images and Cooking Recipes
Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko +3
In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we prop…
A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
Ferda Ofli, Umair Qazi, Muhammad Imran +4
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 intelli…
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza +3
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident…
Fight Detection from Still Images in the Wild
Şeymanur Aktı, Ferda Ofli, Muhammad Imran +1
Detecting fights from still images shared on social media is an important task required to limit the distribution of violent scenes in order to prevent their negative effects. For…
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
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 car…
Landslide Detection in Real-Time Social Media Image Streams
Ferda Ofli, Muhammad Imran, Umair Qazi +4
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches…
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
Firoj Alam, Tanvirul Alam, Md. Arid Hasan +3
Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based…
Mapping Vulnerable Populations with AI
Benjamin Kellenberger, John E. Vargas-Muñoz, Devis Tuia +6
Humanitarian actions require accurate information to efficiently delegate support operations. Such information can be maps of building footprints, building functions, and populatio…
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Firoj Alam, Umair Qazi, Muhammad Imran +1
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large vol…
Robust Training of Social Media Image Classification Models for Rapid Disaster Response
Firoj Alam, Tanvirul Alam, Muhammad Imran +1
Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content ar…
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
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 disaster…
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Muhammad Imran, Firoj Alam, Umair Qazi +2
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,…
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Firoj Alam, Hassan Sajjad, Muhammad Imran +1
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters. The \textit{crisis informatics} research com…