Advances in Nano Research

Volume 16, Number 6, 2024, pages 531-548

DOI: 10.12989/anr.2024.16.6.531

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

Weihua Luo , Ahmed H. Janabi , Joffin Jose Ponnore , Hanadi Hakami , Hakim AL Garalleh , Riadh Marzouki , Yuanhui Yu , Hamid Assilzadeh

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.

Key Words

Convolutional Neural Networks (CNNs); multi-spectral imagery; remote sensing; quantum sensors; urban infrastructure management; XGBoost

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