| title | AI-Driven Disaster Response and Displacement Monitoring |
|---|---|
| authors | Noora Al-Emadi, Muhammad Imran, Yin Yang, Ingmar Weber, Fabjan Lashi, Gaia Rigodanza, Ivana Hajzmanova, Ferda Ofli |
| date | 2026-10-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.21482 |
| affiliation | Humanitarian AI, Displacement Monitoring |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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 pressing challenges. For instance, in addressing climate change, AI-powered models are being used for faster and more accurate global weather forecasting, control and design of renewable energy, and monitoring deforestation. Machine learning algorithms analyze satellite imagery and socioeconomic data to predict poverty and promote sustainable development. AI has also transformed disaster response and conflict monitoring, providing innovative solutions for assessing damage and urgent needs and tracking displacement. |
cite
@misc{alemadi2026ai,
title={AI-Driven Disaster Response and Displacement Monitoring},
author={Noora Al-Emadi and Muhammad Imran and Yin Yang and Ingmar Weber and Fabjan Lashi and Gaia Rigodanza and Ivana Hajzmanova and Ferda Ofli},
year={2026},
eprint={2610.21482},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.21482}
}