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Convolutional neural network based hurricane damage detection using satellite images

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Abstract

Hurricanes are tropical storms that cause immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the first responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a new Convolutional Neural Network model has been designed with the help of satellite images captured from the areas affected by hurricanes. The model will be able to assess the damage by detecting damaged and undamaged buildings based upon which the relief aid can be provided to the affected people on an immediate basis. The model is composed of five convolutional layers, five pooling layers, one flattening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23,000 images of size 128 × 128 pixels has been used in this paper. The proposed model is simulated on 5750 test images at a learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95 and precision of 0.97. The proposed model will help the emergency responders to determine whether there has been damage or not due to the hurricane and also help those to provide relief aid to the affected people.

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Data availability statement

The data has been taken from Kaggle https://www.kaggle.com/kmader/satellite-images-of-hurricane-damage.

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Acknowledgements

This work was supported by Taif University Researchers Supporting Project Number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.

Funding

This work was supported by Taif university Researchers Supporting Project Number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Swapandeep Kaur & Sheifali Gupta

  2. Department of Electronics and Communication Engineering, University Institute of Technology, Himachal Pradesh University, Shimla, 171005, India

    Swati Singh

  3. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttrakhand, 248007, India

    Deepika Koundal

  4. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia

    Atef Zaguia

Authors
  1. Swapandeep Kaur
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  2. Sheifali Gupta
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  3. Swati Singh
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  4. Deepika Koundal
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  5. Atef Zaguia
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Contributions

SK -idea for the article, performed the literature search, prepared the dataset, design the CNN model, conceived the experiment, wrote the Paper. SG, AZ and SS – Conceptualization, drafted and critically revised the work, formal analysis, analyzing and interpretation of data, supervision. DK, AZ—Formal analysis, drafted and critically revised the work, supervision.

Corresponding author

Correspondence to Deepika Koundal.

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The authors declare that they have no conflicts of interest to report regarding the present study.

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Communicated by Irfan Uddin.

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Kaur, S., Gupta, S., Singh, S. et al. Convolutional neural network based hurricane damage detection using satellite images. Soft Comput 26, 7831–7845 (2022). https://doi.org/10.1007/s00500-022-06805-6

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