Core-S1RTC

# 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:
```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}
}
```

Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://huggingface.co/ESA-philab)

# Core-S1RTC

本数据集包含全球覆盖的Sentinel-1（辐射地形校正，RTC）图像斑块，每个斑块尺寸为1,068 × 1,068像素。

| 数据源 | 传感类型 | 斑块总数 | 斑块尺寸 | 总像素数 |
|--------|----------|----------|----------|----------|
| Sentinel-1 RTC | 合成孔径雷达（Synthetic Aperture Radar, SAR） | 1,469,955 | 1,068 × 1,068（10 m） | 超过1.676万亿 |

## 数据内容

| 列名 | 详情 | 分辨率 |
|------|------|--------|
| VV | VV极化接收的线性功率 | 10 m |
| VH | VH极化接收的线性功率 | 10 m |
| thumbnail | 经缩放的假彩色缩略图，保存为PNG格式 | 10 m |

¹ 假彩色合成采用分贝尺度数值，红、绿、蓝通道分别定义为`R:VV G:VV+VH B:VH`。对每个通道应用对比度拉伸操作，将像素值的最小值-最大值区间映射至0-255。这意味着偏蓝区域的VH值相对更高，而亮度由总信号强度决定。由于每张缩略图均进行了归一化处理，其对比度仅在单张样本内有效，无法跨不同样本进行比较。

## 空间覆盖范围
本数据集为全球单时相数据集，几乎所有被Sentinel-1捕获的地球地表区域均至少包含一次（仅少数边缘重叠区域除外，且每个区域仅出现一次）。受传感器覆盖范围限制，本数据集的覆盖范围比Core Sentinel-2数据集低约35%。
下图展示了空间覆盖范围（仅黑色像素为缺失区域）：

## 示例用途
接口脚本可在https://github.com/ESA-PhiLab/Major-TOM获取。以下为缩略图预览示例代码：
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文件编号
ROW_INDEX = 42 # 行号（每个parquet文件约含500行）

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)

## 引用
[](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}
}

由欧洲空间局（European Space Agency, ESA）Φ实验室提供支持 🛰️，链接：https://huggingface.co/ESA-philab