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jfloresf
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mlstac-demo

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Tags:
clouds
sentinel-2
image-segmentation
deep-learning
remote-sensing
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  1. README.md +106 -0
  2. header.png +3 -0
  3. main.json +710 -0
  4. test/0000001.safetensors.gz +3 -0
  5. test/0000002.safetensors.gz +3 -0
  6. test/0000003.safetensors.gz +3 -0
  7. test/0000004.safetensors.gz +3 -0
  8. test/metadata.parquet +3 -0
  9. train/0000001.safetensors.gz +3 -0
  10. train/0000002.safetensors.gz +3 -0
  11. train/0000003.safetensors.gz +3 -0
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  18. train/000000A.safetensors.gz +3 -0
  19. train/000000B.safetensors.gz +3 -0
  20. train/000000C.safetensors.gz +3 -0
  21. train/metadata.parquet +3 -0
  22. validation/0000001.safetensors.gz +3 -0
  23. validation/0000002.safetensors.gz +3 -0
  24. validation/0000003.safetensors.gz +3 -0
  25. validation/0000004.safetensors.gz +3 -0
  26. validation/metadata.parquet +3 -0
README.md ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ tags:
5
+ - clouds
6
+ - sentinel-2
7
+ - image-segmentation
8
+ - deep-learning
9
+ - remote-sensing
10
+ pretty_name: cloudsen12
11
+ ---
12
+ # cloudsen12
13
+
14
+ ***``A dataset about clouds from Sentinel-2``***
15
+
16
+ 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.
17
+ 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: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
18
+
19
+
20
+ **ML-STAC Snippet**
21
+ ```python
22
+ import mlstac
23
+ secret = 'https://huggingface.co/datasets/jfloresf/mlstac-demo/resolve/main/main.json'
24
+ train_db = mlstac.load(secret, framework='torch', stream=True, device='cpu')
25
+ ```
26
+
27
+ <p align="center">
28
+ <img src="header.png" />
29
+ </p>
30
+
31
+ **Sensor: Sentinel2 - MSI**
32
+
33
+ **ML-STAC Task: TensorToTensor, TensorSegmentation**
34
+
35
+ **Data raw repository: [https://cloudsen12.github.io/](https://cloudsen12.github.io/)**
36
+
37
+ **Dataset discussion: [https://github.com/IPL-UV/ML-STAC/discussions/2](https://github.com/IPL-UV/ML-STAC/discussions/2)**
38
+
39
+ **Review mean score: 5.0**
40
+
41
+ **Split_strategy: random**
42
+
43
+ **Paper: [https://www.nature.com/articles/s41597-022-01878-2](https://www.nature.com/articles/s41597-022-01878-2)**
44
+ ## Data Providers
45
+
46
+ |Name|Role|URL|
47
+ | :---: | :---: | :---: |
48
+ |Image & Signal Processing|['host']|https://isp.uv.es/|
49
+ |ESA|['producer']|https://www.esa.int/|
50
+
51
+ ## Curators
52
+
53
+ |Name|Organization|URL|
54
+ | :---: | :---: | :---: |
55
+ |Jair Flores|OEFA|http://jflores.github.io/|
56
+
57
+ ## Reviewers
58
+
59
+ |Name|Organization|URL|Score|
60
+ | :---: | :---: | :---: | :---: |
61
+ |Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|5|
62
+
63
+ ## Labels
64
+
65
+ |Name|Value|
66
+ | :---: | :---: |
67
+ |clear|0|
68
+ |thick-cloud|1|
69
+ |thin-cloud|2|
70
+ |cloud-shadow|3|
71
+
72
+ ## Dimensions
73
+
74
+ ### input
75
+
76
+ |Axis|Name|Description|
77
+ | :---: | :---: | :---: |
78
+ |0|C|Spectral bands|
79
+ |1|H|Height|
80
+ |2|W|Width|
81
+
82
+ ### target
83
+
84
+ |Axis|Name|Description|
85
+ | :---: | :---: | :---: |
86
+ |0|C|Hand-crafted labels|
87
+ |1|H|Height|
88
+ |2|W|Width|
89
+
90
+ ## Spectral Bands
91
+
92
+ |Name|Common Name|Description|Center Wavelength|Full Width Half Max|Index|
93
+ | :---: | :---: | :---: | :---: | :---: | :---: |
94
+ |B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|
95
+ |B02|blue|Band 2 - Blue - 10m|496.5|53.0|1|
96
+ |B03|green|Band 3 - Green - 10m|560.0|34.0|2|
97
+ |B04|red|Band 4 - Red - 10m|664.5|29.0|3|
98
+ |B05|red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4|
99
+ |B06|red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5|
100
+ |B07|red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6|
101
+ |B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7|
102
+ |B8A|red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8|
103
+ |B09|water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9|
104
+ |B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10|
105
+ |B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11|
106
+ |B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12|
header.png ADDED

Git LFS Details

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  • Pointer size: 132 Bytes
  • Size of remote file: 1.65 MB
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+ {
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+ "description": "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.\n 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: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.",
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+ "extent": {
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+ "spatial": {
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+ "bbox": [
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+ [
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+ 0,
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+ 0,
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+ 0,
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+ 0
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+ ],
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+ 0,
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+ },
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+ "temporal": {
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+ "interval": [
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+ [
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+ "2020-01-01T00:00:00",
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+ "2020-01-01T00:00:00"
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+ ],
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+ [
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+ "2020-01-01T00:00:00",
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+ "2020-01-01T00:00:00"
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+ ],
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+ [
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+ "2020-01-01T00:00:00",
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+ "2020-01-01T00:00:00"
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+ ]
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+ ]
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+ }
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+ },
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+ "id": "cloudsen12",
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+ "keywords": [
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+ "clouds",
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+ "sentinel-2",
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+ "image-segmentation",
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+ "deep-learning",
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+ "remote-sensing"
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+ ],
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+ "license": "cc-by-4.0",
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+ "links": [
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+ {
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+ "href": "./main.json",
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+ "rel": "root",
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+ "type": "application/json",
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+ "title": null
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+ }
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+ ],
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+ {
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+ "name": "Image & Signal Processing",
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+ "roles": [
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+ "host"
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+ ],
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+ "url": "https://isp.uv.es/"
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+ },
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+ {
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+ "name": "ESA",
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+ "roles": [
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+ "producer"
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+ ],
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+ "url": "https://www.esa.int/"
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+ }
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+ ],
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+ "stac_extensions": [
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+ "https://stac-extensions.github.io/contacts/v0.1.1/schema.json"
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+ ],
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+ "stac_version": "1.0.0",
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+ "title": "A dataset about clouds from Sentinel-2",
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+ "type": "Collection",
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+ "ml:authors": {
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+ "authors": [
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+ {
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+ "name": "David Montero",
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+ "organization": "RSC4Earth",
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