Citation

Yap, Yong Loong and Jatmiko, Wisnu and Azizah, Kurniawati and Hilman, Muhammad Hafizhuddin and Sin, Liang Lim (2025) Comparative Study of Building Detection from Satellite Images using Deep Learning. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

In this paper, the performances of various deep learning models, including YOLOv8, ResNet, and VGG across a spectrum of configurations and training scenarios are studied. The aims of this study are (i) to develop and implement a deep learning model for accurately detecting building damage in satellite images and (ii) to develop methodologies for quantifying and analysing building damage level. By comparing with various Convolutional Neural Network (CNN) models, this research aims to improve the accuracy and efficiency of building detection and damage assessment, ultimately aiding in urban planning and disaster response efforts. This comparative analysis not only highlights the relative advantages of each model but also guides the selection of appropriate deep learning techniques for enhancing building detection accuracy. By optimizing these deep learning models, we aim to advance the capabilities of satellite image-based monitoring systems, thus supporting more efficient urban planning and more responsive disaster management strategies

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Building detection; Convolutional neural networks
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	17 Mar 2026 08:27
Last Modified:	17 Mar 2026 08:27
URII:	http://shdl.mmu.edu.my/id/eprint/15530

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