All the files mentioned in the article on Towards Data Science Neural Network for Landsat Classification Using Tensorflow in Python | A step-by-step guide.

Source code and files mentioned in the medium post titled "Is CNN equally shiny on mid-resolution satellite data?" available at https://towardsdatascience.com/is-cnn-equally-shiny-on-mid-resolution-satellite-data-9e24e68f0c08

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This repository contains a deep learning workflow for Land Use and Land Cover (LULC) classification using Landsat 8 and 9 imagery. The project leverages multispectral satellite data, spectral indices, and digital elevation models (DEM) to train a Conv1D neural network for accurate land cover mapping.

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images.