Land Cover Image Classification

2024/01/17 by Antonio Rangel, Rangel, Antonio, Juan Terven +5 · 1 citation
Earth and Planetary Sciences · Engineering · Environmental Science · #Artificial intelligence #Civil engineering #Computer science #Contextual image classification #Convolutional neural network #Cover (algebra) #Deep learning #Engineering #Geography #Image (mathematics) #Land Use and Ecosystem Services #Land cover #Land use #Machine learning #Remote Sensing and Land Use #Remote sensing #Remote-Sensing Image Classification #Satellite #Satellite image #Satellite imagery #Transformer

paper · pdf · doi:10.48550/arxiv.2401.09607

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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