AI-Driven Disaster Response and Displacement Monitoring

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. In addressing climate change, AI-powered models are being used for faster and more accurate global weather forecasting, the control and design of renewable energy systems, and the monitoring of 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, identifying urgent needs, and tracking displacement. This article showcases two projects that exemplify the potential of AI-driven approaches for disaster response and displacement monitoring with case studies from the Arab world. The first case study highlights how social media data was utilized to support disaster response efforts during the devastating 2023 Türkiye–Syria earthquake. By processing vast amounts of unstructured social media content, AI-powered tools enabled real-time situational awareness, facilitating more targeted and efficient humanitarian interventions. The second case study focuses on the use of satellite imagery to monitor internal displacement during the Syrian civil war. Through the development of a region-specific vehicle detection model, we demonstrate how vehicle counts in satellite images can serve as proxies for tracking population movements. This innovative approach addresses critical data gaps in conflict-affected regions, offering a scalable solution for monitoring displacement trends in near real time.