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Core-S2L1C
Contains a global coverage of Sentinel-2 (Level 1C) patches, each of size 1,068 x 1,068 pixels.
Source | Sensing Type | Number of Patches | Patch Size | Total Pixels |
---|---|---|---|---|
Sentinel-2 Level-1C | Optical Multispectral | 2,245,886 | 1,068x1,068 | 2.56 Trillion |
Content
Column | Details | Resolution |
---|---|---|
B01 | Coastal aerosol, 442.7 nm (S2A), 442.3 nm (S2B) | 60m |
B02 | Blue, 492.4 nm (S2A), 492.1 nm (S2B) | 10m |
B03 | Green, 559.8 nm (S2A), 559.0 nm (S2B) | 10m |
B04 | Red, 664.6 nm (S2A), 665.0 nm (S2B) | 10m |
B05 | Vegetation red edge, 704.1 nm (S2A), 703.8 nm (S2B) | 20m |
B06 | Vegetation red edge, 740.5 nm (S2A), 739.1 nm (S2B) | 20m |
B07 | Vegetation red edge, 782.8 nm (S2A), 779.7 nm (S2B) | 20m |
B08 | NIR, 832.8 nm (S2A), 833.0 nm (S2B) | 10m |
B8A | Narrow NIR, 864.7 nm (S2A), 864.0 nm (S2B) | 20m |
B09 | Water vapour, 945.1 nm (S2A), 943.2 nm (S2B) | 60m |
B10 | SWIR – Cirrus, 1373.5 nm (S2A), 1376.9 nm (S2B) | 60m |
B11 | SWIR, 1613.7 nm (S2A), 1610.4 nm (S2B) | 20m |
B12 | SWIR, 2202.4 nm (S2A), 2185.7 nm (S2B) | 20m |
cloud_mask | Cloud Mask produced by SEnSeI | 10m |
thumbnail | RGB composite [B04, B03, B02] saved as png | 10m |
Spatial Coverage
This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-2 is contained at least once in this dataset (and only once, excluding some marginal overlaps).
The following figure demonstrates the spatial coverage (only black pixels are absent):
Example Use
Interface scripts are available at https://github.com/ESA-PhiLab/Major-TOM
Here's a sneak peek with a thumbnail image:
from fsspec.parquet import open_parquet_file
import pyarrow.parquet as pq
from io import BytesIO
from PIL import Image
PARQUET_FILE = 'part_03900' # parquet number
ROW_INDEX = 42 # row number (about 500 per parquet)
url = "https://huggingface.co/datasets/Major-TOM/Core-S2L1C/resolve/main/images/{}.parquet".format(PARQUET_FILE)
with open_parquet_file(url,columns = ["thumbnail"]) as f:
with pq.ParquetFile(f) as pf:
first_row_group = pf.read_row_group(ROW_INDEX, columns=['thumbnail'])
stream = BytesIO(first_row_group['thumbnail'][0].as_py())
image = Image.open(stream)
Cite
@inproceedings{Major_TOM,
title={Major TOM: Expandable Datasets for Earth Observation},
author={Alistair Francis and Mikolaj Czerkawski},
year={2024},
booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
eprint={2402.12095},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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