Remote sensing data analyzed by machine learning to predict structural changes

Title

Remote sensing data analyzed by machine learning to predict structural changes

Creator

Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P.

Description

Natural disasters can cause extensive structural damage, necessitating rapid and reliable post-event assessment to support emergency response and recovery planning. Although several methods exist for pixel-level damage classification using post-disaster imagery, translating these outputs into meaningful, building-wise assessments remains challenging. Building-level damage prediction provides more interpretable insights, enabling a clearer estimation of the severity of impact on individual structures and a comprehensive understanding of the overall destruction. This information is crucial for quantifying damage magnitude and prioritizing relief operations. This paper proposes Damage Estimation U-Net (DE-U-Net), a deep learning framework designed to estimate structural damage across four classes: No Damage, Minor Damage, Major Damage, and Destroyed. The model is trained on the xBD dataset to learn representative damage patterns. DE-U-Net is developed by integrating a modified Siamese U-Net with a Damage Ratio Analyzer (DRA) algorithm for building-level damage conversion. The DRA algorithm comprises three components: (1) Connected Component Analysis (CCA) to transform pixel-level predictions into building-level predictions (2) size filtering to remove noise and eliminate small artifacts, and (3) a damage estimation module to compute the number of pixels corresponding to each damage class per building. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026.

Source

International Journal of System Assurance Engineering and Management;

Date

01-01-2026

Publisher

Springer

Subject

Building damage assessment; Convolutional neural networks; Image processing; Machine learning; Remote sensing; Satellite images; Supervised model

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, Kerala, Cheruthuruthy, Thrissur, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 9756809;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s13198-026-03199-8

https://www.scopus.com/pages/publications/105034348274?origin=resultslist