AI-Driven Disaster Response and Displacement Monitoring

Noora Al-Emadi, Muhammad Imran, Yin Yang, Ingmar Weber, Fabjan Lashi +3 more
10/15/2026
cs.AIcs.CV

No confident code implementation found for this paper. 3 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Foundation Models for Remote Sensing and Earth Observation: A Survey

83% match1 repo522024

Earth Virtualization Engines -- A Technical Perspective

83% match2023

Machine Learning and Citizen Science Approaches for Monitoring the Changing Environment

83% match2023

Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring

83% match2023

AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

82% match2024

On the Opportunities of Green Computing: A Survey

82% match2023

Cite this paper

@article{alemadi2026aidriven,
  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    = {http://arxiv.org/abs/2610.21482v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2610.21482.svg)](https://codeofpaper.com/paper/2610.21482)

Discussion