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Smart Classification of Damaged Buildings through Remote Sensing

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The framework effectively classifies damage states in earthquake-affected buildings using remote sensing data.
Binary classification of building damage identified over fifty percent accurate in previous applications.
Utilizing high-resolution InSAR images, multi-class damage classification offers innovative insight for damage assessment.
The study highlights the first use of CNN models for building damage assessment with InSAR imagery, marking a significant advancement.

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Observational analysis classifies building damage from earthquakes using remote sensing techniques, suggesting a new framework for assessment.

V Ankitha (2025) studied this question.

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Observational analysis classifies building damage from earthquakes using remote sensing techniques, suggesting a new framework for assessment.