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

Suman Kunwar1,*, Jannatul Ferdush2

1 Faculty of Computer Science, Selinus University of Sciences and Literature, Ragusa, Italy
2 Deparment of Computer Science and Engineering, Jashore University of Science and Technology, Jashore, Bangladesh

* Corresponding Author: Suman Kunwar. Email: email

(This article belongs to the Special Issue: Applications of Artificial Intelligence in Geomatics for Environmental Monitoring)

Revue Internationale de Géomatique 2024, 33, 1-13. https://doi.org/10.32604/rig.2023.047627

Abstract

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.

Graphical Abstract

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

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APA Style
Kunwar, S., Ferdush, J. (2024). Mapping of land use and land cover (LULC) using eurosat and transfer learning. Revue Internationale de Géomatique, 33(1), 1-13. https://doi.org/10.32604/rig.2023.047627
Vancouver Style
Kunwar S, Ferdush J. Mapping of land use and land cover (LULC) using eurosat and transfer learning. Revue Internationale de Gomatique . 2024;33(1):1-13 https://doi.org/10.32604/rig.2023.047627
IEEE Style
S. Kunwar and J. Ferdush, "Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning," Revue Internationale de Gomatique , vol. 33, no. 1, pp. 1-13. 2024. https://doi.org/10.32604/rig.2023.047627



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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