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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Identifying Informal Settlements Using Contourlet Assisted Deep Learning.

Rizwan Ahmed Ansari1, Rakesh Malhotra1, Krishna Mohan Buddhiraju2

  • 1Department of Environmental, Earth and Geospatial Sciences, North Carolina Central University, Durham, NC 27707, USA.

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Summary
This summary is machine-generated.

This study introduces a novel multiscale deep learning approach to accurately map informal urban settlements. The method enhances detection capabilities, providing crucial data for urban planning and land-use management in developing countries.

Keywords:
contourlet transformdeep learninginformal settlementsmultiresolutionremote sensingsemantic segmentation

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Area of Science:

  • Urban planning and remote sensing
  • Geographic Information Systems (GIS) and spatial analysis
  • Artificial Intelligence (AI) and machine learning for geospatial applications

Background:

  • Rapid urbanization and rural-to-urban migration lead to the proliferation of unplanned informal settlements in developing countries.
  • Accurate mapping of informal settlements is challenging due to their complex, unstable, and variable characteristics, hindering effective urban planning and land-use management.
  • Existing methods struggle to reliably detect and delineate informal built-up areas, highlighting a critical data gap.

Purpose of the Study:

  • To develop and evaluate an integrated multiscale deep learning approach for the semantic segmentation and mapping of informal urban settlements.
  • To investigate the effectiveness of combining U-net architecture with multiscale contourlet transform for improved feature extraction in complex urban environments.
  • To analyze the impact of wavelet and contourlet decompositions within the U-net framework on the accuracy of informal settlement detection.

Main Methods:

  • Proposed a composite deep learning architecture integrating U-net with multiscale contourlet transform for semantic segmentation.
  • Employed wavelet and contourlet decompositions to enhance feature representation at multiple scales.
  • Evaluated the method's performance using standard metrics: precision, recall, F-score, mean intersection over union (mIoU), and overall accuracy.

Main Results:

  • The proposed integrated approach demonstrated superior class-discriminating power compared to existing methods.
  • Achieved high overall classification accuracy, ranging from 94.9% to 95.7%.
  • The analysis confirmed the benefits of multiscale decompositions in improving the U-net architecture's performance for mapping informal settlements.

Conclusions:

  • The developed multiscale deep learning method offers a robust and accurate solution for mapping informal urban settlements.
  • This research provides valuable tools for urban planners and policymakers to better understand and manage informal areas.
  • The findings contribute to advancing geospatial AI techniques for addressing critical urban development challenges.