Land Cover Image Classification

2024/01/17 by Antonio Rangel, Rangel, Antonio, Juan Terven +5
Engineering · Earth and Planetary Sciences · Environmental Science · #Remote-Sensing Image Classification #Remote Sensing and Land Use #Land Use and Ecosystem Services

paper · pdf · doi:10.48550/arxiv.2401.09607

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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