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Disaster Detection Framework for Smart Cities: An AI YOLOv8 and IoT Approach

Year: 2025

Type: conference-paper

Abstract: This paper proposes a disaster detection framework tailored for smart cities, focusing on fire and flood incidents. By integrating the YOLOv8 AI algorithm on a Raspberry Pi IoT device, the system ensures real-time detection and rapid alerting through IoT networks. The experimental setup validates the model’s ability to detect fire efficiently using simple devices, paving the way for scalable and cost-effective solutions for urban resilience and safety. The proposed approach aligns with smart city infrastructure and contributes to the growing field of AI-driven disaster response.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15270892

Author: Kamel, Hossam

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6912024523

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