Automatic Identification of Hurricane Damage Using a Transfer Learning Approach with Satellite Images

Authors

  • Vinston Raja R. Associate Professor Department of Information Technology, Panimalar Engineering College, Chennai
  • Deanbeemarao B. Assistant Professor Department of Computer Science and Engineering, Panimalar Engineering College, Chennai
  • Jimson L. Assistant Professor, Department of Computer science and Engineering DMI College of Engineering
  • Angel Kiruba D. R. Assistant Professor, Department of Computer science and Engineering DMI College of Engineering
  • Gnanaprakasam C. Assistant Professor Grade - I, Dept of Artificial Intelligence and Data Science, Panimalar Engineering College

Keywords:

CNN - Convolution Neural Network, Satellite imagery, Tensor Flow

Abstract

Satellite imagery may be pre-processed and processed to extract color, texture, and form capabilities, which may then be fed into a CNN version. In education, the CNN version, styles and features that correspond to regions impacted by hurricanes may be discovered, as well as styles and features that correspond to areas no longer impacted. A CNN model also can be constructed using other statistics sorts, such as geographic and meteorological statistics. By combining distinct varieties of records, it's miles possible to enhance the version's accuracy and robustness, as well as offer complete know-how of storm impacts. CNNs can provide treasured insights into the storm harm category in emergency reaction efforts and resource corporations the usage of satellite TV for pc imagery.

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References

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Published

23.02.2024

How to Cite

Raja R., V. ., B., D. ., L., J. ., D. R., A. K. ., & C., G. . (2024). Automatic Identification of Hurricane Damage Using a Transfer Learning Approach with Satellite Images. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 389–399. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4851

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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