EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
| Autor: | Benjamin Bischke, Damian Borth, Andreas Dengel, Patrick Helber |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Atmospheric Science Earth observation Computer Science - Machine Learning 010504 meteorology & atmospheric sciences Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction 0211 other engineering and technologies Computer Science - Computer Vision and Pattern Recognition computer science 02 engineering and technology Land cover computer.software_genre 01 natural sciences Convolutional neural network Machine Learning (cs.LG) Computers in Earth Sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences Land use business.industry Deep learning Spectral bands Satellite Data mining Artificial intelligence business computer |
| Popis: | 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 27 000 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 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 . |
| Databáze: | OpenAIRE |
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