Core-S1RTC

Contains a global coverage of Sentinel-1 (RTC) patches, each of size 1,068 x 1,068 pixels.

Source	Sensing Type	Number of Patches	Patch Size	Total Pixels
Sentinel-1 RTC	Synthetic Aperture Radar	1,469,955	1,068 x 1,068 (10 m)	> 1.676 Trillion

Content

Column	Details	Resolution
VV	Received Linear Power in the VV Polarization	10m
VH	Received Linear Power in the VV Polarization	10m
thumbnail	Rescaled false colour1 saved as png	10m

¹ False colour composites are made with decibel-scale values with red green and blue defined as R:VV G:VV+VH B:VH. For each channel, a contrast-stretch is applied, transforming minimum-maximum to 0-255. This means bluer areas have relatively higher VH values, whilst brightness is a function of overall intensity. This is relative within each thumbnail because of the normalisation, and so cannot be compared across different samples.

Spatial Coverage

This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-1 is contained at least once in this dataset (and only once, excluding some marginal overlaps). The coverage is about 35% lower than for Core Sentinel-2 dataset due to the sensor coverage limitations.

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-S1RTC/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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