Authors:
Jamila Shaaban Ebrahim, Jouma Ali Al-Mohamad, Muhammad Fasehullah, Norlaile Salleh Hudin, Raja Rajeswari Ponnusamy, J. Adeline Sneha

Addresses:
Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Aleppo University, Aleppo, Syria. Department of Computer and Mobile Communication Engineering, Faculty of Information Engineering, Al-Shahbaa Private University, Aleppo, Syria. College of Engineering and Technology, Southwest University, Beibei District, Chongqing, China. Department of Business Management and Entrepreneurship, Faculty of Management and Economics, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia. School of Computing, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Wilayah Persekutuan, Malaysia.

Abstract:

This paper examines the transformative role of Artificial Intelligence (AI), particularly computer vision and machine learning, as a strategic tool for damage assessment and infrastructure prioritization in Syria's post-conflict reconstruction. Following the political transition of December 2024, Syria faces the unprecedented challenge of rebuilding physical infrastructure devastated by over a decade of civil war, with the World Bank estimating reconstruction costs between $140 billion and $345 billion. Traditional manual damage assessment methods are inherently slow, incomplete, and potentially biased, creating critical bottlenecks in resource allocation and reconstruction planning. This paper synthesizes insights from AI literature, post-conflict reconstruction frameworks, and Syria-specific applications—including drone-based remote sensing in Kafr Nabl, deep learning damage detection from satellite imagery, the SKAI tool developed by WFP and Google Research, and the AI-driven multi-criteria decision framework for prioritizing structural interventions—to propose an integrated AI-enabled reconstruction framework. The framework addresses three interconnected pillars: rapid damage assessment using computer vision and remote sensing, evidence-based infrastructure prioritization using AI-driven multi-criteria decision-making, and capacity building for sustainable AI adoption. The paper argues that AI is not merely a technical tool but a strategic enabler that can accelerate recovery, optimize scarce resources, and build a more transparent and accountable reconstruction process. Key recommendations include establishing a national AI-enabled damage assessment system, adopting AI-driven prioritization frameworks, investing in local AI capacity building, and ensuring that AI systems are designed with principles of transparency, accountability, and inclusion.

Keywords: Artificial Intelligence; Computer Vision; Damage Assessment; Infrastructure Prioritization; Post-Conflict Reconstruction; Machine Learning; Remote Sensing; Deep Learning; Decision Support Systems.

Received on: 17/08/2025, Revised on: 28/10/2025, Accepted on: 09/12/2025, Published on: 09/06/2026

DOI: 10.69888/FTSIS.2026.000777

FMDB Transactions on Sustainable Intelligence and Security, 2026 Vol. 1 No. 2, Pages: 151-167

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