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Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing

Kim VanExel1, Samendra Sherchan2, Siyan Liu3

  • 1Bioenvironmental Sciences Department, Morgan State University, Baltimore, MD 21251, USA.

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|February 25, 2025
PubMed
Summary

Artificial intelligence (AI) models accurately detect natural disasters like flooding and desertification from aerial images. This AI approach offers a novel solution for environmental monitoring and climate change adaptation.

Keywords:
AICNNUAVsclimate changedesertificationfloodingneural networksremote sensingsatellitetransfer learning

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Climate change is increasing the frequency and intensity of natural disasters.
  • Effective monitoring and early detection of these disasters are crucial for mitigation and response.
  • Aerial imagery provides valuable data for disaster assessment, but automated analysis is challenging.

Purpose of the Study:

  • To develop and evaluate artificial intelligence (AI) models for detecting natural disasters, specifically flooding and desertification, from aerial images.
  • To create and utilize a novel dataset, the Climate Change Dataset, for training and comparing deep learning models.
  • To demonstrate the potential of AI in addressing environmental challenges and supporting climate change adaptation.

Main Methods:

  • A new dataset, the Climate Change Dataset, was compiled with 6334 aerial images from UAVs and satellites.
  • Four machine learning (ML) models, including convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50, were trained.
  • Model performance was compared, and DenseNet201 was selected for optimization. Cross-validation was performed.

Main Results:

  • All four ML models demonstrated high performance in detecting natural disasters.
  • DenseNet201 and ResNet50 achieved the highest testing accuracies at 99.37% and 99.21%, respectively.
  • The models successfully classified images into three categories: Flooded, Desert, and Neither.

Conclusions:

  • AI, particularly deep learning models, shows significant potential for accurate and efficient natural disaster detection from aerial imagery.
  • The developed Climate Change Dataset and optimized ML models offer a valuable resource for environmental monitoring and research.
  • This AI-driven approach can enhance disaster response, contribute to environmental sustainability, and aid in climate change adaptation strategies.