Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

被引:0
|
作者
Kunwar, Suman [1 ]
Ferdush, Jannatul [2 ]
机构
[1] Selinus Univ Sci & Literature, Fac Comp Sci, Ragusa, Italy
[2] Jashore Univ Sci & Technol, Dept Comp Sci & Engn, Jashore, Bangladesh
来源
关键词
Land use land cover; EuroSAT; transfer learning; convolutional neural networks; vision transformers; CLASSIFICATION; BENCHMARK; DATASET;
D O I
10.32604/rig.2023.047627
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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.
引用
收藏
页码:1 / 13
页数:13
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