TY  - EJOU
AU  - Kunwar, Suman 
AU  - Ferdush, Jannatul 

TI  - Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning
T2  - Revue Internationale de Géomatique

PY  - 2024
VL  - 33
IS  - 1
SN  - 2116-7060

AB  - As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in Artificial Intelligence (AI), computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with red-green-blue (RGB) bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies.
KW  - Land use land cover; EuroSAT; transfer learning; convolutional neural networks; vision transformers

DO  - 10.32604/rig.2023.047627