Land Cover Image Classification

Antonio Rangel, Juan Terven, Diana M. Cordova-Esparza, E. A. Chavez-Urbiola
1/17/2024
cs.CVcs.LGeess.IV

No official code found. Best implementation: jrterven/eurosat_classification (10 stars, MIT, last commit 2023-11-02).

Abstract

Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis. We compare convolutional neural networks (CNN) against transformer-based methods, showcasing their applications and advantages in LC studies. We used EuroSAT, a patch-based LC classification data set based on Sentinel-2 satellite images and achieved state-of-the-art results using current transformer models.

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Code Implementations(1)

jrterven/eurosat_classificationCommunity100%
MIT

Land Cover Classificatio using the EuroSAT dataset

103Python, Jupyter NotebookNov 2, 20232 years agoMIT

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Cite this paper

@article{rangel2024land,
  title  = {Land Cover Image Classification},
  author = {Antonio Rangel and Juan Terven and Diana M. Cordova-Esparza and E. A. Chavez-Urbiola},
  year   = {2024},
  eprint = {2401.09607},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.09607v1}
}

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