ItemArticle

Advancing Building Damage Classification Accuracy through Machine Learning-Based Model Design using High Resolution Remote Sensing Images

Title
Advancing Building Damage Classification Accuracy through Machine Learning-Based Model Design using High Resolution Remote Sensing Images
Creator
Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P
Description
The ability to evaluate the damage to buildings both accurately and precisely is essential for disaster recovery, planning, and rescue services. This paper proposes a new approach based on integrating machine learning algorithms in building damage classification. To achieve higher precision in classifying the level of building damage, this research proposes a new technique that employs machine learning strategies. The researchers were able to train the model to be able to differentiate the different levels of building damage and the feature extraction was performed through machine learning. The model effectively extracts and learns multiple complex signals which represent different degrees of damage from a well picked database which include several degrees of damage. In a single pass, the Siamese U-Net can perform feature extraction and similarity measurement between two different images. The efficiency and effectiveness of the Siamese U-net model can be increased by reducing inference time, thus increasing its ability to deliver faster predictions while also improving its accuracy. The suggested Enhanced U-Net (EU-Net) could greatly increase the accuracy of building-level classification. As it turned out, the results are very promising and reach beyond traditional approaches with bringing more sample opportunities of machine learning integration in the building damage assessment context. Additionally, this study believes that the accuracy of building damage classification can be further enhanced demonstrating the usefulness of machine learning in disaster management. 2025 World Scientific Publishing Company.
Source
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems;Volume;33;Issue;5;pp.665-683
Date
01-01-2025
Publisher
World Scientific
Subject
accuracy; classification; damage assessment; Image processing; machine learning; remote sensing
Coverage
Sajitha I., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Kerala, Thrissur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 2184885; CODEN: IJUSF
Format
online
Language
English
Type
Article

Citation

Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P, “Advancing Building Damage Classification Accuracy through Machine Learning-Based Model Design using High Resolution Remote Sensing Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23020.

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