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BigEarthNet
Classification
Remote Sensing
|...
License: Custom

Overview

The BigEarthNet archive was constructed by the Remote Sensing Image Analysis (RSiM)
Group and the Database Systems and Information Management (DIMA)
Group at the Technische Universität Berlin (TU Berlin).
This work is supported by the European Research Council under the ERC Starting Grant BigEarth
and by the German Ministry for Education and Research as Berlin Big Data Center (BBDC).

BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2
image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017
and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania,
Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles
were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting
tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image
patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided
from the CORINE Land Cover database of the year 2018 (CLC 2018).

BigEarthNet is significantly
larger than the existing archives in remote sensing and opens up promising directions to advance
research for the analysis of large-scale remote sensing image archives. It is also very convenient
to be used as a training source in the context of deep learning for knowledge discovery from
big archives in remote sensing.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{sumbul2019bigearthnet,
  title={Bigearthnet: A large-scale benchmark archive for remote sensing image understanding},
  author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{\"u}m and Markl, Volker},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={5901--5904},
  year={2019},
  organization={IEEE}
}

License

Custom

Data Summary
Type
Image,
Amount
590.326K
Size
65.22GB
Provided by
ERC(European Research Council)
The ERC complements other funding activities in Europe such as those of the national research funding agencies, and is a flagship component of Horizon 2020, the European Union's Research Framework Programme for 2014 to 2020.
| Amount 590.326K | Size 65.22GB
BigEarthNet
Classification
Remote Sensing
License: Custom

Overview

The BigEarthNet archive was constructed by the Remote Sensing Image Analysis (RSiM)
Group and the Database Systems and Information Management (DIMA)
Group at the Technische Universität Berlin (TU Berlin).
This work is supported by the European Research Council under the ERC Starting Grant BigEarth
and by the German Ministry for Education and Research as Berlin Big Data Center (BBDC).

BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2
image patches. To construct BigEarthNet, 125 Sentinel-2 tiles acquired between June 2017
and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania,
Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles
were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting
tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image
patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided
from the CORINE Land Cover database of the year 2018 (CLC 2018).

BigEarthNet is significantly
larger than the existing archives in remote sensing and opens up promising directions to advance
research for the analysis of large-scale remote sensing image archives. It is also very convenient
to be used as a training source in the context of deep learning for knowledge discovery from
big archives in remote sensing.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{sumbul2019bigearthnet,
  title={Bigearthnet: A large-scale benchmark archive for remote sensing image understanding},
  author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{\"u}m and Markl, Volker},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={5901--5904},
  year={2019},
  organization={IEEE}
}

License

Custom

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