Land Cover Classification using Artificial Neural Networks in R with TensorFlow and Keras

Land cover classification using remote sensing and artificial neural networks is an interesting and important topic in the field of environmental science. Remote sensing allows us to gather information about the Earth’s surface, including vegetation, soil, and water, from a distance. This information is critical for understanding and managing our environment. Land cover classification is the process of categorizing the Earth’s surface into different classes based on its characteristics. This can be done using remote sensing data and artificial neural networks (ANNs), which are a type of machine learning algorithm that can learn to recognize patterns in data. ANNs have been shown to be effective at classifying land cover from remote sensing data, and can be implemented in the R programming language using the TensorFlow and Keras packages. The use of ANNs for land cover classification is interesting because it allows us to automatically classify large amounts of remote sensing data with high accuracy. This can help us better understand changes in land use and land cover over time, which is important for managing our environment and natural resources. Additionally, the use of ANNs for this task is a cutting-edge application of machine learning technology, making it an exciting area of research. <a href="https://ai.plainenglish.io/land-cover-classification-using-artificial-neural-networks-in-r-with-tensorflow-and-keras-a95a33460b2a">Read More</a>