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  11. A New Framework With Convoluted Oscillatory Neural Network For Efficient Object-based Land Use And Land Cover Classification On Remote Sensing Images

A new framework with convoluted oscillatory neural network for efficient object-based land use and land cover classification on remote sensing images

Chirag Jitendra Chandnani1, Shlok Chetan Kulkarni1, Geraldine Bessie Amali D1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu.

Frontiers in Artificial Intelligence
|January 2, 2026

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View abstract on PubMed

Summary
This summary is machine-generated.

Convoluted Oscillatory Neural Networks (CONN) improve land use land cover classification for urban planning. This novel approach enhances accuracy in mapping environmental changes caused by urbanization.

Area of Science:

  • Environmental Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Rapid urbanization in areas like Pune, India, has led to environmental challenges such as flash floods and landslides.
  • Effective land use land cover (LULC) classification is crucial for managing urban development and mitigating environmental impacts.
  • Conventional Convolutional Neural Networks (CNNs) have limitations in generalization due to single hyperplane decision boundaries per neuron.

Purpose of the Study:

  • To propose a novel framework, Convoluted Oscillatory Neural Networks (CONN), for accurate LULC classification.
  • To leverage the benefits of oscillatory activation functions for improved model generalization and accuracy.
  • To analyze the man-made impact on the environment through effective LULC mapping in the Pune region.

Main Methods:

Keywords:
LANDSAT-8convolution neural networkland use land cover (LULC)oscillatory functions

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  • Development of a novel framework combining CNN architecture with oscillatory activation functions (CONN).
  • Utilizing LANDSAT-8 surface reflectance images for LULC classification in the Pune area.
  • Training and evaluating the CONN model, specifically with the Decaying Sine Unit, against conventional CNN models.

Main Results:

  • The proposed CONN model achieved a high overall train accuracy of 99.999% and a test accuracy of 95.979%.
  • CONN demonstrated superior performance compared to conventional CNN models in precision, recall, and User's Accuracy.
  • A comprehensive ablation study validated the model's performance across different feature set subsets.

Conclusions:

  • The novel CONN framework effectively enhances LULC classification accuracy, outperforming traditional CNNs.
  • Oscillatory activation functions offer significant advantages for improving the generalization capabilities of deep learning models in remote sensing.
  • The findings provide a valuable tool for urban planning and environmental management in rapidly urbanizing regions.
remote sensing