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Land Cover Image Classification (2401.09607v1)

Published 17 Jan 2024 in cs.CV, cs.LG, and eess.IV

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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Authors (4)
  1. Antonio Rangel (3 papers)
  2. Juan Terven (4 papers)
  3. Diana M. Cordova-Esparza (2 papers)
  4. E. A. Chavez-Urbiola (1 paper)
Citations (1)
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