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Publication

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

Patrick Helber; Benjamin Bischke; Damian Borth; Andreas Dengel
In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS-2018), Tutorial MO3.R6: Land Cover Dynamics I, July 22-27, Valencia, Spain, Pages 204-207, ISBN 978-1-5386-7150-4, IEEE, 2018.

Abstract

In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Network (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards a number of Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.