Advances in nano research

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Combination of fuzzy models via economic management for city multi-spectral remote sensing nano imagery road target

  • Weihua Luo (Jiangxi Zhonggantou Survey & Design Co.,Ltd) ;
  • Ahmed H. Janabi (Computer Techniques Engineering Department, College of Engineering & Technology, Al-Mustaqbal University) ;
  • Joffin Jose Ponnore (Department of Mechanical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University) ;
  • Hanadi Hakami (Department of Software Engineering, College of Engineering, University of Business and Technology) ;
  • Hakim AL Garalleh (Department of Mathematical Science, College of Engineering, University of Business and Technology) ;
  • Riadh Marzouki (Department of Chemistry, College of Science, King Khalid University) ;
  • Yuanhui Yu (School of Civil and Environmental Engineering, Georgia Institute of Technology) ;
  • Hamid Assilzadeh (Institute of Research and Development, Duy Tan University)
  • Received : 2021.06.06
  • Accepted : 2022.02.13
  • Published : 2024.06.25
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Abstract

The study focuses on using remote sensing to gather data about the Earth's surface, particularly in urban environments, using satellites and aircraft-mounted sensors. It aims to develop a classification framework for road targets using multi-spectral imagery. By integrating Convolutional Neural Networks (CNNs) with XGBoost, the study seeks to enhance the accuracy and efficiency of road target identification, aiding urban infrastructure management and transportation planning. A novel aspect of the research is the incorporation of quantum sensors, which improve the resolution and sensitivity of the data. The model achieved high predictive accuracy with an MSE of 0.025, R-squared of 0.85, RMSE of 0.158, and MAE of 0.12. The CNN model showed excellent performance in road detection with 92% accuracy, 88% precision, 90% recall, and an f1-score of 89%. These results demonstrate the model's robustness and applicability in real-world urban planning scenarios, further enhanced by data augmentation and early stopping techniques.

Keywords

Acknowledgement

This study is supported via funding from Prince Sattam Bin Abdulaziz University, (PSAU/2024/R/1445), AlKharj, Saudi Arabia. The authors extend their appreciation to the Al-Mustaqbal University for supporting this work under grant number (MUC - E- 0122).

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