Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning
cs.CV, cs.AI, cs.LG
Submitted: 2023-11-06
Updated: 2023-11-06
Comments: 10 pages, 7 figures
Journal ref: Revue Internationale de Géomatique, 33(1), 1-13 (2024)
Code: https://github.com/sumn2u/LULC-Mapping
License: http://creativecommons.org/licenses/by/4.0/
The gist: As the global population continues to expand, the demand for natural resources increases.
Terminology
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 AI, computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with 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.
Sources
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- Parameter-efficient Model Adaptation for Vision Transformers
- Why gradient clipping accelerates training: A theoretical justification for adaptivity
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