Convolutional Neural Network Methods for Detecting Land-Use Changes


  • Smita Sunil Burrewar Research Scholar, Department of Architecture and Planning, National Institute of Technology, Patna.
  • Mazharul Haque Assistant Professor, Department of Architecture and Planning, National Institute of Technology, Patna.
  • Tanwir Uddin Haider Associate professor, Department of Computer Science & Engineering, National Technology of Engineering, Patna.


Land use Land cover changes, Convolutional neural networks (CNN), remote sensing, Discrete Wavelet Transform (DWT), Satellite Image Classification, Urbanization


As the demand for reliable, up-to-date information about natural and manmade environments has risen, so has attention to this problem. Due to urbanization, it must contend with rapid climatic changes. To lower the urban heat island, both existing and rising cities require accurate land cover classification, which enables changes in settlement distribution, bodies of water, and vegetation index to be recognized. Images from space and the air are gathered from different sources and categorized by characteristics. Further research is needed on convolutional neural networks (CNN), which have been implemented progressively in land-use classification. CNN approaches are tested for land classification and land use (LU) change detection in this study. To solve a practical challenge caused by a shortage of data, the CNN and faster recurrent neural network (R-CNN) models were trained utilizing data from two sources. The statistics showed that while green spaces and low-density residential areas diminished over time, residential areas with higher densities rose with time, indicating the pattern of LU community transformation in Nagpur's study area and the technique's accuracy of 98.86%.


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How to Cite

Burrewar, S. S. ., Haque, M. ., & Haider, T. U. . (2024). Convolutional Neural Network Methods for Detecting Land-Use Changes. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 573–590. Retrieved from



Research Article