# Land Cover Image Classification

Source: [Land Cover Image Classification](<https://www.sota2.com/research/paper/68fa71d51e209131cea533e9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:13:28.899Z

Authors: Antonio Rangel, Juan Terven, Diana M. Cordova-Esparza, E.A. Chavez-Urbiola

Published: 2024-01-17T21:32:04.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.09607>)

[PDF](<https://arxiv.org/pdf/2401.09607.pdf>)