Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models
elhalawani, M., El-Mewafi, M., Zarzora, F., Shaaban, M. (2024). Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models. EKB Journal Management System, 7(3), 78-86. doi: 10.21608/dusj.2024.433446
Mariem A. elhalawani; Mahmoud El-Mewafi; Fawzi Zarzora; Mohammed Shaaban. "Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models". EKB Journal Management System, 7, 3, 2024, 78-86. doi: 10.21608/dusj.2024.433446
elhalawani, M., El-Mewafi, M., Zarzora, F., Shaaban, M. (2024). 'Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models', EKB Journal Management System, 7(3), pp. 78-86. doi: 10.21608/dusj.2024.433446
elhalawani, M., El-Mewafi, M., Zarzora, F., Shaaban, M. Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models. EKB Journal Management System, 2024; 7(3): 78-86. doi: 10.21608/dusj.2024.433446

Assigning accuracy for Cars Detection from High-Resolution Satellite Images Using Different Machine Learning Models

Delta University Scientific Journal
Volume 7, Issue 3, November 2024, Page 78-86  XML PDF (872.93 K)
Document Type: Original research papers
DOI: 10.21608/dusj.2024.433446
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Authors
Mariem A. elhalawani email 1; Mahmoud El-Mewafi2; Fawzi Zarzora3; Mohammed Shaaban3
1Teaching Assistant, Basic Science department, Delta University for Science and Technology, 7731168 Gamasa, Egypt
2Professor, Civil Engineering department, Mansoura university,35516 Mansoura, Egypt.
3Assistant Professor, Civil Engineering department, Mansoura university,35516 Mansoura, Egypt
Abstract
High-resolution satellite imagery provides a wealth of detailed visual information that can be leveraged for various machine learning applications. In this study, we present a deep learning-based approach for car classification using high-resolution satellite images. Utilizing the powerful capabilities of Tensor Flow layers, we design and implement a convolutional neural network (CNN) to accurately identify and classify different types of cars from satellite imagery. The process involves the collection of a diverse dataset of satellite images containing vehicles, followed by rigorous data pre-processing and augmentation to enhance model robustness. The CNN architecture is optimized through hyper parameter tuning and trained on a labeled dataset, achieving high accuracy in classifying vehicles into predefined categories such as sedans, SUVs, and trucks. Our results demonstrate the effectiveness of using deep learning models with TensorFlow layers for car classification tasks, highlighting the potential for broader applications in urban planning, traffic management, and automated vehicle detection from satellite imagery.
Keywords
High; Resolution Satellite Images; Deep Learning; Car Classification; TensorFlow
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