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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Mohd Jawed Khan1, Pankaj Pratap Singh1, Biswajeet Pradhan2,3

  • 1Department of Computer Science & Engineering, Central Institute of Technology, Kokrajhar 783370, Assam, India.

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Summary

This study introduces an advanced deep learning model for automated road network extraction from remote sensing images. The novel approach significantly improves road segmentation accuracy, outperforming existing methods.

Keywords:
Archimedes optimization algorithmartificial intelligenceconvolutional neural networksdilated convolutionsquantum computingremote sensingroad extraction

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automated road network extraction from remote sensing (RS) imagery is crucial but challenging due to diverse road characteristics.
  • Traditional methods struggle with varying road features, necessitating advanced techniques for high-resolution segmentation.

Purpose of the Study:

  • To propose and evaluate the Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology.
  • To enhance the accuracy and efficiency of road extraction from RS data using deep learning and optimization algorithms.

Main Methods:

  • The ATP-QDCNNRE method utilizes a quantum dilated convolutional neural network (QDCNN) model, incorporating quantum computing concepts and dilated convolutions.
  • Hyperparameter tuning is performed using the Archimedes optimization algorithm (AOA) to optimize the QDCNN model for road extraction.
  • The model captures local and global contextual information while preserving spatial resolution for fine road feature extraction.

Main Results:

  • The ATP-QDCNNRE method achieved high performance on the Massachusetts road dataset.
  • Key metrics include an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 score of 90.85%, precision of 87.54%, and recall of 94.41%.

Conclusions:

  • The ATP-QDCNNRE method demonstrates superior efficiency and accuracy for road extraction from remote sensing imagery.
  • The integration of deep learning, quantum computing concepts, and advanced optimization significantly enhances segmentation capabilities.