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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 and Calculation of Roadway Area from Satellite Images Using Improved Deep Learning Model and

Varun Yerram1, Hiroyuki Takeshita2, Yuji Iwahori2

  • 1Department of Electronics & Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.

Journal of Imaging
|May 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for calculating roadway area using advanced U-net++ and ResNeXt models for precise road extraction from satellite images. The approach enhances urban planning and remote sensing accuracy.

Keywords:
Pix2PixResNeXtU-net++area calculationdeep learning modelroadway extractionsatellite images

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Area of Science:

  • Remote Sensing
  • Urban Planning
  • Geospatial Analysis

Background:

  • Roadway area calculation is a novel challenge in remote sensing and urban planning.
  • Accurate road extraction from satellite imagery is crucial for urban development and management.

Purpose of the Study:

  • To propose an effective method for calculating roadway area from satellite images.
  • To enhance the accuracy of roadway extraction using deep learning architectures.

Main Methods:

  • A two-step approach: roadway extraction followed by area calculation.
  • Utilizing novel U-net++ and ResNeXt convolutional neural network architectures.
  • Implementing an efficient post-processing technique to improve overlap with ground truth data.

Main Results:

  • The proposed method achieves superior performance in road extraction compared to existing U-net family models.
  • The approach demonstrates high accuracy in calculating roadway area using pixel resolution from satellite images.
  • Evaluation on the Massachusetts dataset confirms the effectiveness of the novel architecture and post-processing.

Conclusions:

  • The developed method offers a significant advancement in automated roadway area calculation.
  • This technique provides a robust solution for urban planning and remote sensing applications.
  • The combination of U-net++ and ResNeXt architectures presents a state-of-the-art solution for road extraction.