File size: 2,438 Bytes
8d3e320 58e7a70 8d3e320 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 964c124 df6df78 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
license: cc-by-nc-4.0
---
# **CloudSEN12**
## **A Benchmark Dataset for Cloud Semantic Understanding**
CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are
evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2
levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR),
digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge
cloud detection algorithms.
CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of
hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our
paper.
Ready to start using **[CloudSEN12](https://cloudsen12.github.io/)**?
**[Download Dataset](https://cloudsen12.github.io/download.html)**
**[Paper - Scientific Data](https://www.nature.com/articles/s41597-022-01878-2)**
**[Inference on a new S2 image](https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb)**
**[Enter to cloudApp](https://github.com/cloudsen12/CloudApp)**
**[CloudSEN12 in Google Earth Engine](https://gee-community-catalog.org/projects/cloudsen12/)**
<br>
### **Example**
```py
import numpy as np
# Read high-quality train
train_shape = (8490, 512, 512)
B4X = np.memmap('train_B04.dat', dtype='int16', mode='r', shape=train_shape)
y = np.memmap('train_target.dat', dtype='int8', mode='r', shape=train_shape)
# Read high-quality val
val_shape = (535, 512, 512)
B4X = np.memmap('val_B04.dat', dtype='int16', mode='r', shape=val_shape)
y = np.memmap('val_target.dat', dtype='int8', mode='r', shape=val_shape)
# Read high-quality test
test_shape = (975, 512, 512)
B4X = np.memmap('test_B04.dat', dtype='int16', mode='r', shape=test_shape)
y = np.memmap('test_target.dat', dtype='int8', mode='r', shape=test_shape)
```
<br>
This work has been partially supported by the Spanish Ministry of Science and Innovation project
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the
**[SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php)**.
|