Land Cover Image Classification

• URL: http://arxiv.org/abs/2401.09607v1
• Date: Wed, 17 Jan 2024 21:32:04 GMT
• Title: Land Cover Image Classification
• Authors: Antonio Rangel, Juan Terven, Diana M. Cordova-Esparza, E.A. Chavez-Urbiola
• Abstract summary: Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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