Convolutional Neural Network Methods for Detecting Land-Use Changes
Keywords:
Land use Land cover changes, Convolutional neural networks (CNN), remote sensing, Discrete Wavelet Transform (DWT), Satellite Image Classification, UrbanizationAbstract
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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