Agricultural Land-Use Classification on Satellite Data Using Machine Learning
Data publikacji: 20 cze 2025
Zakres stron: 219 - 232
Otrzymano: 15 wrz 2024
Przyjęty: 12 gru 2024
DOI: https://doi.org/10.2478/bsrj-2025-0011
Słowa kluczowe
© 2025 Thao-Ngan Nguyen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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.