Land Cover Image Classification

TL;DR

This paper compares CNN and transformer-based deep learning models for land cover image classification, demonstrating that transformer models achieve state-of-the-art accuracy on satellite image datasets, improving efficiency and reducing human error.

Contribution

It introduces a comparative analysis of CNN and transformer models for land cover classification, highlighting the superior performance of transformer architectures.

Findings

Transformer models outperform CNNs in accuracy.

State-of-the-art results achieved on EuroSAT dataset.

Transformers offer improved efficiency in LC analysis.

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.