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Related Experiment Video

Updated: Jan 18, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Deep Learning Image-Based Classification for Post-Earthquake Damage Level Prediction Using UAVs.

Norah Alsaaran1, Adel Soudani1

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Unmanned Aerial Vehicles (UAVs) with lightweight deep learning models enable rapid post-earthquake damage assessment. This study shows MobileNetV3-Small on edge devices provides efficient, real-time structural damage prediction for search and rescue teams.

Keywords:
MobileNetconvolutional neural network (CNN)deep learningearthquakeedge computingreal-time damage assessmentunmanned aerial vehicle (UAV)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Unmanned Aerial Vehicles (UAVs) are crucial for disaster response.
  • Image-based damage assessment requires efficient processing.
  • Lightweight deep learning models offer potential for onboard analysis.

Purpose of the Study:

  • To investigate the MobileNetV3-Small model for real-time post-earthquake damage assessment using UAV imagery.
  • To evaluate the model's performance and efficiency on edge devices.

Main Methods:

  • Utilized the MobileNetV3-Small Convolutional Neural Network (CNN) model.
  • Trained the model to classify three damage levels (none, moderate, severe).
  • Deployed and tested the model on a Raspberry Pi 5 for edge computing.

Main Results:

  • MobileNetV3-Small achieved the lowest FLOPs, outperforming ShuffleNetv2 by 58.8%.
  • Fine-tuning improved accuracy by 4.5% with a minimal FLOPs increase.
  • Achieved a weighted average F-score of 0.93 on a merged dataset.
  • Demonstrated real-time performance on a Raspberry Pi 5.

Conclusions:

  • Lightweight CNNs like MobileNetV3-Small are effective for onboard UAV image analysis.
  • The model enables efficient, real-time damage assessment in resource-constrained environments.
  • This technology can significantly aid Search and Rescue (SAR) operations post-earthquake.