EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

TL;DR

This paper introduces EuroSAT, a new large-scale satellite image dataset for land use and cover classification, along with benchmarks using deep learning, achieving high accuracy and enabling various Earth observation applications.

Contribution

The paper presents EuroSAT, a comprehensive labeled satellite image dataset with benchmarks, facilitating research in land classification using deep learning.

Findings

Achieved 98.57% classification accuracy

Provided a publicly available dataset for land use classification

Demonstrated applications in land change detection and map improvement

Abstract

In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.