Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Manasi Shitole,

  • Arati Bhujbal,

  • Vaibhav Godase,

  1. Student, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  2. Student, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
  3. Assistant Professor, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India

Abstract

Natural disasters such as floods, earthquakes, and wildfires cause significant damage to human life and infrastructure every year. Rapid and accurate assessment of the affected areas is essential for effective disaster response and recovery planning. Traditional image-based analysis using single-sensor data often fails under adverse conditions such as cloud cover, smoke, or poor lighting. To overcome these limitations, this study proposes a novel framework for disaster impact assessment using multi-sensor satellite data fusion that integrates optical, Synthetic Aperture Radar (SAR), and thermal imagery. The proposed system employs deep learning-based feature extraction and fusion techniques to enhance the spatial and temporal understanding of affected regions. A U-Net-based convolutional neural network (CNN) is utilized for change detection between pre- and post-disaster images, while the fused data improves robustness against missing or noisy inputs. The framework was validated using Sentinel-1 (SAR) and Sentinel-2 (optical) datasets on recent flood and earthquake events. Experimental results demonstrate that the proposed method achieves improved accuracy (IoU: 87.3%) compared to single-sensor approaches, effectively identifying damaged regions under complex environmental conditions. The study concludes that multi-sensor fusion offers a reliable and scalable solution for real-time disaster monitoring, supporting faster decision-making in emergency management systems, facilitating effective allocation of resources and bolstering worldwide disaster resilience.

Keywords: Disaster Assessment, Remote Sensing, Multi-Sensor Fusion, Satellite Imagery, Change Detection, Deep Learning

How to cite this article:
Manasi Shitole, Arati Bhujbal, Vaibhav Godase. Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach. International Journal of Satellite Remote Sensing. 2026; 04(01):-.
How to cite this URL:
Manasi Shitole, Arati Bhujbal, Vaibhav Godase. Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach. International Journal of Satellite Remote Sensing. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijsrs/article=2026/view=240227


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Ahead of Print Subscription Review Article
Volume 04
01
Received 18/12/2025
Accepted 09/03/2026
Published 17/04/2026
Publication Time 120 Days
19

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