| Item Type |
Journal paper
|
| Abstract |
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. |
| Authors |
Patrick, Helber; Benjamin, Bischke; Andreas, Dengel & Damian, Borth |
| Research Team |
AIML Lab |
| Journal or Publication Title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Language |
English |
| Subjects |
computer science |
| HSG Classification |
contribution to scientific community |
| Refereed |
Yes |
| Date |
July 2019 |
| Publisher |
IEEE |
| Volume |
12 |
| Number |
7 |
| Page Range |
2217-2226 |
| Number of Pages |
10 |
| Publisher DOI |
https://doi.org/10.1109/JSTARS.2019.2918242 |
| Official URL |
https://ieeexplore.ieee.org/abstract/document/8736... |
| Depositing User |
Prof. Dr. Damian Borth
|
| Date Deposited |
26 Oct 2019 21:39 |
| Last Modified |
20 Jul 2022 17:39 |
| URI: |
https://www.alexandria.unisg.ch/publications/258199 |
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
