6 papers
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…