SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany

This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.

Contents

Each sample includes:

• 3 10m resolution bands (RGB), 120px x 120px
• 1 10m resolution band (infrared), 120px x 120px
• 6 20m resolution bands, 60px x 60px
• 2 60m resolution bands, 20xp x 20px
• 1 pixel-level label map
• 2 binary masks for cloud and snow coverage
• 2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively
• 1 JSON-file containing additional meta-information

The meta.csv contains the following information about each sample:

• Which season it belongs to
• Which of the two grids it belongs to
• Coordinates of the patch center
• Whether it was acquired from Sentinel-2 Satellite A or B
• Date and time of image acquisition
• Snow and cloud coverage percentages
• Image-level multi-class labels
• Three additional image-level urbanization labels, based on the center pixel (details below)
• The path to the sample

Classes

Urbanization classes

• SLRAUM
  • 0: None
  • 1: Ländlicher Raum (~ rural area)
  • 2: Städtischer Raum (~ urban area)
• RTYP3
  • 0: None
  • 1: Ländliche Regionen (~ rural areas)
  • 2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)
  • 3: Städtische Regionen (~ urban areas)
• KTYP4
  • 0: None
  • 1: Dünn besiedelte ländliche Kreise
  • 2: Kreisfreie Großstädte
  • 3: Ländliche Kreise mit Verdichtungsansätzen
  • 4: Städtische Kreise

Further information on the urbanization classes can be found here:

SLRAUM

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html

RTYP3

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html

KTYP4

https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html

License of landcover model

Bundesamt für Kartographie und Geodäsie

dl-de/by-2-0 from https://www.govdata.de/dl-de/by-2-0

© GeoBasis-DE / BKG 2022

Source of landcover model

https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/