bigearthnet

The BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels.

To construct the 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).

Bands and pixel resolution in meters: B01: Coastal aerosol; 60m B02: Blue; 10m B03: Green; 10m B04: Red; 10m B05: Vegetation red edge; 20m B06: Vegetation red edge; 20m B07: Vegetation red edge; 20m B08: NIR; 10m B09: Water vapor; 60m B11: SWIR; 20m B12: SWIR; 20m B8A: Narrow NIR; 20m

License: Community Data License Agreement - Permissive, Version 1.0.

URL: http://bigearth.net/

bigearthnet is configured with tfds.image.bigearthnet.BigearthnetConfig and has the following configurations predefined (defaults to the first one):

  • rgb (v0.0.2) (Size: ?? GiB): Sentinel-2 RGB channels

  • all (v0.0.2) (Size: ?? GiB): 13 Sentinel-2 channels

bigearthnet/rgb

Sentinel-2 RGB channels

Versions:

  • 0.0.2 (default):

Statistics

None computed

Features

FeaturesDict({
    'filename': Text(shape=(), dtype=tf.string),
    'image': Image(shape=(120, 120, 3), dtype=tf.uint8),
    'labels': Sequence(ClassLabel(shape=(), dtype=tf.int64, num_classes=43)),
    'metadata': FeaturesDict({
        'acquisition_date': Text(shape=(), dtype=tf.string),
        'coordinates': FeaturesDict({
            'lrx': Tensor(shape=(), dtype=tf.int64),
            'lry': Tensor(shape=(), dtype=tf.int64),
            'ulx': Tensor(shape=(), dtype=tf.int64),
            'uly': Tensor(shape=(), dtype=tf.int64),
        }),
        'projection': Text(shape=(), dtype=tf.string),
        'tile_source': Text(shape=(), dtype=tf.string),
    }),
})

Homepage

Supervised keys (for as_supervised=True)

(u'image', u'labels')

bigearthnet/all

13 Sentinel-2 channels

Versions:

  • 0.0.2 (default):

Statistics

None computed

Features

FeaturesDict({
    'B01': Tensor(shape=[20, 20], dtype=tf.float32),
    'B02': Tensor(shape=[120, 120], dtype=tf.float32),
    'B03': Tensor(shape=[120, 120], dtype=tf.float32),
    'B04': Tensor(shape=[120, 120], dtype=tf.float32),
    'B05': Tensor(shape=[60, 60], dtype=tf.float32),
    'B06': Tensor(shape=[60, 60], dtype=tf.float32),
    'B07': Tensor(shape=[60, 60], dtype=tf.float32),
    'B08': Tensor(shape=[120, 120], dtype=tf.float32),
    'B09': Tensor(shape=[20, 20], dtype=tf.float32),
    'B11': Tensor(shape=[60, 60], dtype=tf.float32),
    'B12': Tensor(shape=[60, 60], dtype=tf.float32),
    'B8A': Tensor(shape=[60, 60], dtype=tf.float32),
    'filename': Text(shape=(), dtype=tf.string),
    'labels': Sequence(ClassLabel(shape=(), dtype=tf.int64, num_classes=43)),
    'metadata': FeaturesDict({
        'acquisition_date': Text(shape=(), dtype=tf.string),
        'coordinates': FeaturesDict({
            'lrx': Tensor(shape=(), dtype=tf.int64),
            'lry': Tensor(shape=(), dtype=tf.int64),
            'ulx': Tensor(shape=(), dtype=tf.int64),
            'uly': Tensor(shape=(), dtype=tf.int64),
        }),
        'projection': Text(shape=(), dtype=tf.string),
        'tile_source': Text(shape=(), dtype=tf.string),
    }),
})

Homepage

Citation

@article{Sumbul2019BigEarthNetAL,
  title={BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding},
  author={Gencer Sumbul and Marcela Charfuelan and Beg{"u}m Demir and Volker Markl},
  journal={CoRR},
  year={2019},
  volume={abs/1902.06148}
}