DETECTION OF POST-EARTHQUAKE DAMAGE SEVERITY FROM SATELLITE IMAGES USING VGG19

DETECTION OF POST-EARTHQUAKE DAMAGE SEVERITY FROM SATELLITE IMAGES USING VGG19

Registration ID: IJNRD_215469

Published ID: IJNRD2403323

DOI: Click Here to Get

V.V.Nagamani , Adichalwar Sravika , Chigurumamidi Sanjana , Doda Pavani

Remote sensing data, Ground shaking intensity maps, Color-coded satellite images, Emergency response.

Effective assessment of building damage following earthquakes is crucial for prompt emergency response and allocation of resources. In this study, we propose an integrated approach that combines high-resolution building inventory data, earthquake ground shaking intensity maps, and post-event InSAR imagery analysis aided by recent advances in machine learning algorithms. Our methodology involves the utilization of ensemble models in a machine learning framework to classify the damage state of buildings affected by earthquakes. We leverage post event very high-resolution remote sensing imagery to identify collapsed buildings, with a particular focus on the potential of Convolutional Neural Networks (CNNs) in extracting deep features for this purpose. We compare the performance of CNN features with texture features using the Random Forest classifier. Additionally, we employ VGG19, a pre-trained deep learning model, to gain insights into the defining characteristics of images in terms of shape, color, and structure. The results of our approach are visualized through color-coded satellite images, where completely damaged buildings are represented in red, partially collapsed buildings in blue, and basically intact buildings in green. Regions marked in red denote areas requiring urgent assistance and support. The integration of remote sensing data, machine learning algorithms, and visual representations enhances the effectiveness of earthquake damage assessment and aids in facilitating timely and targeted emergency response efforts.

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"DETECTION OF POST-EARTHQUAKE DAMAGE SEVERITY FROM SATELLITE IMAGES USING VGG19", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d169-d177, March-2024, Available :https://ijnrd.org/papers/IJNRD2403323.pdf

Volume 9 Issue 3, March-2024

Pages : d169-d177

Paper Reg. ID: IJNRD_215469

Published Paper Id: IJNRD2403323

Downloads: 000121251

Research Area: Information Technology 

Country: Medchal , Malkajgiri, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403323.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403323

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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