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---
license: cc-by-sa-4.0
tags:
- earth-observation
- remote-sensing
- sentinel-1
- sar
- synthethic-aperture-radar
- satellite
- geospatial
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
features:
- name: product_id
dtype: string
- name: grid_cell
dtype: string
- name: product_datetime
dtype: string
- name: thumbnail
dtype: image
- name: vv
dtype: binary
- name: vh
dtype: binary
configs:
- config_name: default
data_files: images/*.parquet
- config_name: metadata
data_files: metadata.parquet
---
# 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 colour<sup>1</sup> saved as png | 10m |
<sup>1</sup> 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:
```python
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
[](https://arxiv.org/abs/2402.12095/)
```latex
@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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