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

IN: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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Alexandria

In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demon- strate 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.

Helber Patrick, Bischke Benjamin, Dengel Andreas, Borth Damian

26 Oct 2019

Item Type
Journal paper
Journal Title
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Language
English
Subject Areas
computer science