Agricultural Land-Use Classification on Satellite Data Using Machine Learning

Background The utilization of satellite images has become increasingly popular for detecting land usage, focusing on agricultural land classification in recent years, due to the significant decline in bees. Objectives This paper seeks to address these challenges by applying several machine learning algorithms on multi-spectral satellite data from Sentinel-2 to derive accurate land classification models. Methods/Approach Specifically, we use five bands: Red, Green, Blue, NIR, and NDVI to build three models, namely Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). Results Our results show that the CNN model outperforms the other algorithms on collected satellite data, with an accuracy score of 0.82, F1-score of 0.72, and AUC score of 0.94, followed by the RF and LSTM models. Conclusions This highlights the importance of utilizing advanced machine learning techniques, particularly CNNs, in accurately classifying agricultural land use changes.