ItemArticle

Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images

Title
Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images
Creator
Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P.
Description
We propose the development of a robust Enhanced U-Net framework for detecting building objects in images compromised by collapse. Traditional approaches often struggle to identify smaller buildings obstructed by taller structures, trees, or cloud coverage. However, recent advancements in machine learning algorithms present promising opportunities to address these challenges and improve the accuracy of building object detection and damage assessment. The proposed method employs the Siamese U-Net framework, enhanced with novel machine learning algorithms to overcome limitations in existing methodologies and increase the accuracy and reliability of damage assessment, even in complex scenarios. By using augmented satellite images during testing and lowering the building threshold value, our model can accurately predict damaged buildings and retrieve the footprints of smaller structures. The results of this research will advance image analysis techniques, especially in scenarios where collapsed structures pose significant identification and damage assessment challenges. This will be invaluable for government disaster management agencies, insurance companies, and other related organizations. 2025 World Scientific Publishing Company.
Source
International Journal of Modeling, Simulation, and Scientific Computing;Issue;;Article No.;2541019;
Date
01-01-2025
Publisher
World Scientific
Subject
augmentation; building footprints; convolutional neural networks; disaster management; image processing; machine learning; Remote sensing; satellite images
Coverage
Sajitha I., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST 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: 17939623;
Format
online
Language
English
Type
Article

Citation

Sajitha, I.; Sambandam, Rakoth Kandan; John, Saju P., “Improvized machine learning model for extracting building footprints from collapsed images using high-resolution remote sensing images,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23050.

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