IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET

20 citations as recorded by crossref.

1. Supervised desertification classification using Siamese Variational Autoencoder F. Chouikhi et al. https://doi.org/10.1080/19479832.2025.2476544
2. Hybrid deep learning for satellite image classification: Integrating CNN and transformer architectures M. Khlifi et al. https://doi.org/10.1016/j.softx.2026.102754
3. Remote Sensing Based Land Cover Classification Using Residual Feature—Hyper Graph Convolutional Neural Network (HGCNN) L. Gowri et al. https://doi.org/10.1007/s12524-024-02115-6
4. Automated classification of remote sensing satellite images using deep learning based vision transformer A. Adegun et al. https://doi.org/10.1007/s10489-024-05818-y
5. A deep classification model to assess environment following hazards using remote sensing images M. Sahu & R. Dash https://doi.org/10.1007/s42452-025-07445-9
6. A hybrid deep learning model based on vision transformer and convolutional neural networks for land use and land cover classification H. Mahendra et al. https://doi.org/10.1016/j.asoc.2026.114775
7. A comprehensive assessment of vegetation and water resource dynamics in Bihar using Sentinel-2 derived NDVI and NDWI indices A. Thakur et al. https://doi.org/10.1007/s12040-025-02718-8
8. Tourism-Induced Urbanization in Phuket Island, Thailand (1987–2024): A Spatiotemporal Analysis S. Moukomla & W. Marome https://doi.org/10.3390/urbansci9030055
9. Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models N. Rengma & M. Yadav https://doi.org/10.1007/s10661-024-12719-7
10. Optuna-Optimized Pythagorean Fuzzy Deep Neural Network: A Novel Framework for Uncertainty-Aware Image Classification A. Kaya Karakutuk et al. https://doi.org/10.3390/app152011097
11. A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification H. Dastour & Q. Hassan https://doi.org/10.3390/su15107854
12. A CNN-based framework for land use land cover classification of heterogeneous terrain using satellite images A. Tarafdar et al. https://doi.org/10.1007/s00521-025-11314-2
13. Optimizing land use and land cover classification with deep learning on multi-resolution datasets A. Raut & S. Tomar https://doi.org/10.1007/s10661-026-15391-1
14. Agricultural Land Classification Using Vegetation Indices, PCA, and Google Earth Engine: Case Study of Söke/Aydın M. İNALPULAT et al. https://doi.org/10.33202/comuagri.1295054
15. The land use and land cover changes, 1994–2024: implications for livelihood options and employment opportunities in Dhanbad, India A. Ranjan et al. https://doi.org/10.1007/s41324-025-00605-4
16. A privacy-preserving, on-board satellite image classification technique incorporating homomorphic encryption and transfer learning A. Roy et al. https://doi.org/10.3389/frsen.2025.1678882
17. Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan O. Alipbeki et al. https://doi.org/10.3390/agronomy14020268
18. A Comparative Analysis of Supervised Machine Learning Algorithms for Land Use and Land Cover Classification in Google Earth Engine N. Karimazondo et al. https://doi.org/10.1007/s12524-025-02370-1
19. Stacked encoding and AutoML-based identification of lead–zinc small open pit active mines around Rampura Agucha in Rajasthan state, India A. Ojha et al. https://doi.org/10.1038/s41598-025-89672-z
20. Analysis and Forecasting of Temporal Rainfall Variability Over Hundred Indian Cities Using Deep Learning Approaches S. Singh et al. https://doi.org/10.1007/s41748-024-00396-y