Update README.md

README.md

@@ -11,65 +11,158 @@ tags:
   - remote-sensing
 pretty_name: cloudsen12plus
 ---
+
+<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">
+
+  ![Dataset Image](https://tacofoundation.github.io/taco.png)
+  
+<b><p>This dataset follows the TACO specification.</p></b>
+</div>
+
+
 # cloudsen12plus
 
 ****The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2****
 
 
-CloudSEN12+ is a significant extension of the CloudSEN12 dataset, which doubles the number of 
+CloudSEN12+ version 1.1.0 is a significant extension of the CloudSEN12 dataset, which doubles the number of 
 expert-reviewed labels, making it, by a large margin, the largest cloud detection dataset to 
 date for Sentinel-2. All labels from the previous version have been curated and refined, enhancing
-the dataset's trustworthiness. This new release is licensed under CC0, which puts it in the public
+the dataset's truestworthiness. This new release is licensed under CC0, which puts it in the public
 domain and allows anyone to use, modify, and distribute it without permission or attribution.
-The images have been padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the 
+
+The images are padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the 
 patches are divisible by 32. The padding is filled with zeros in the left and bottom sides of the 
 image. For those who prefer traditional storage formats, GeoTIFF files are available in our 
 [ScienceDataBank](https://www.scidb.cn/en/detail?dataSetId=2036f4657b094edfbb099053d6024b08&version=V1)
 repository.
+
+`CloudSEN12+` v.1.1.0 offers three distinct modes, tailored for diverse research and application needs:
+
+- **`cloudsen12-l1c`**: Patches derived from Sentinel-2 Level-1C imagery, including high-quality labels, scribble annotations, and unlabeled data.
+
+- **`cloudsen12-l2a`**: Similar to cloudsen12-l1c but based on Sentinel-2 Level-2A data as processed by Google Earth Engine.
+
+- **`cloudsen12-extra`**: A supplementary collection of metadata to enhance contextual understanding of landscapes. Cloud masks from multiple sources have been normalized to align with the CloudSEN12 class schema. This mode includes:
+  - **`s1_vv:`** Normalized Sentinel-1 Global Backscatter Model Land Surface (VV polarization).
+  - **`s1_vh:`** Normalized Sentinel-1 Global Backscatter Model Land Surface (VH polarization).
+  - **`elevation:`** Elevation data (meters) sourced from the MERIT Hydro dataset.
+  - **`LC10:`** ESA WorldCover 10m v100 land cover product.
+  - **`cloudmask_qa:`** Cloud mask from Sentinel-2 Level-1C.
+  - **`cloudmask_sen2cor:`** Cloud mask from Sentinel-2 Level-2A.
+  - **`cloudmask_cloudscore:`** Cloud detection results from Google Cloud Masking.
+  - **`cloudmask_s2cloudless:`** Cloud mask generated by Sentinel Hub Cloud Detector.
+  - **`cloudmask_unetmobv2:`** A cloud mask produced by a UnetMobV2 model trained on the CloudSEN12 dataset.
+
+<center>
+<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/9UA4U3WObVeq7BAcf37-C.png' alt='drawing' width='50%'/>
+</center>
+
+*CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, 
+respectively. `high`, `scribble`, and `nolabel` refer to the types of expert-labeled annotations*
+
+- Changelog:
+    Version 1.1.0:
+    - We save all GeoTIFF files with discard_lsb=2 to improve the compression ratio.
+    - Fixed 2000x2000 rotated patches. The datapoints are now correctly oriented. Check the patches:
+        - ROI_2526__20200709T105031_20200709T105719_T31UDQ
+        - ROI_0070__20190708T130251_20190708T130252_T24MUA
+        - ROI_4565__20200530T100029_20200530T100502_T32TQP
+    - Improved the quality of the following patches:
+        - ROI_1098__20200515T190909_20200515T191310_T11WPN
+        - ROI_1735__20190814T163849_20190814T164716_T15SXS
+        - ROI_0760__20190516T022551_20190516T022553_T56WMD
+        - ROI_3696__20200419T075611_20200419T080344_T35MRN
+        - ROI_2864__20170529T105621_20170529T110523_T31TCN
+    - We removed the following patches due to poor quality:
+        - ROI_3980__20190228T005641_20190228T005640_T58WDB
+        - ROI_1489__20210228T070831_20210228T070834_T40TDP
+
+- Consideration:
+    - The field `roi_id` field serves as a unique identifier for the geographical location of each patch. In other words, it is used to link S2 images with 
+    a specific geographic location. However, the roi_id between the 509x509 and 2000x2000 patches are 
+    not the same. For example, the roid_id: `ROI_0008` in the 509x509 patches is not the same as the 
+    `ROI_0008` in the 2000x2000 patches. In this version, we fixed this issue by summing the max value 
+    of the 509x509 patches to the 2000x2000 patches. In this way, the `roi_id` between the 509x509 and
+    2000x2000 patches are unique. If users of 2000x2000 patches need to match the original roi_id published
+    in the previous version, they can use the following formula:
+        - `old_roi_id_2000 = old_roi_id_2000 - 12101`
+    where `12101` is the max value of the 509 patches. We also reported the previous roi as old_roi_id.
+
 <center>
 <img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/9UA4U3WObVeq7BAcf37-C.png' alt='drawing' width='50%'/>
 </center>
 *CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, 
 respectively. `high`, `scribble`, and `nolabel` refer to the types of expert-labeled annotations*
 
-## 🌮 TACO Snippet
+
+
+## 🔄 Reproducible Example
+
+<a target="_blank" href="https://colab.research.google.com/drive/1U9n40rwdnn73bdWruONA3hIs1-H3f74Q">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
 
 
 Load this dataset using the `tacoreader` library.
+
 ```python
 import tacoreader
-dataset = tacoreader.load('...')
+import rasterio as rio
+
+print(tacoreader.__version__) # 0.5.2
+
+# Remotely load the Cloud-Optimized Dataset 
+dataset = tacoreader.load("tacofoundation:cloudsen12-l1c")
+#dataset = tacoreader.load("tacofoundation:cloudsen12-l2a")
+
+# Read a sample
+sample_idx = 2422
+s2_l1c = dataset.read(sample_idx).read(0)
+s2_label = dataset.read(sample_idx).read(1)
+
+# Retrieve the data
+with rio.open(s2_l1c) as src, rio.open(s2_label) as dst:    
+    s2_l1c_data = src.read([4, 3, 2], window=rio.windows.Window(0, 0, 512, 512))
+    s2_label_data = dst.read(window=rio.windows.Window(0, 0, 512, 512))
+
+# Display
+fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+ax[0].imshow(s2_l1c_data.transpose(1, 2, 0) / 3000)
+ax[0].set_title("Sentinel-2 L1C")
+ax[1].imshow(s2_label_data[0])
+ax[1].set_title("Human Label")
+plt.tight_layout()
+plt.savefig("taco_check.png")
+plt.close(fig)
 ```
 
+<center>
+<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0nRv7sqMRMNY-TVkY2kh7.png' alt='drawing' width='70%'/>
+</center>
 
 
-Or in R:
-```r
-library(tacoreader)
-dataset <- tacoreader::load('...')
-```
 ## 🛰️ Sensor Information
 
-
 The sensor related to the dataset: **sentinel2msi**
-## 🎯 Task
 
+## 🎯 Task
 
 The task associated with this dataset: **semantic-segmentation**
-## 📂 Original Data Repository
 
+## 📂 Original Data Repository
 
 Source location of the raw data:**[https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus](https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus)**
-## 💬 Discussion
 
+## 💬 Discussion
 
 Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/cloudsen12/discussions](https://huggingface.co/datasets/tacofoundation/cloudsen12/discussions)**
-## 🔀 Split Strategy
 
+## 🔀 Split Strategy
 
 How the dataset is divided for training, validation, and testing: **stratified**
-## 📚 Scientific Publications
 
+## 📚 Scientific Publications
 
 Publications that reference or describe the dataset.
 
@@ -90,9 +183,8 @@ Publications that reference or describe the dataset.
 }
 ```
 
-
-
 ### Publication 02
+
 - **DOI**: [10.1109/IGARSS52108.2023.10282381](10.1109/IGARSS52108.2023.10282381)
 - **Summary**: Exploration of incorrect annotations in cloud semantic segmentation datasets.
 - **BibTeX Citation**:
@@ -107,9 +199,8 @@ Publications that reference or describe the dataset.
 }
 ```
 
-
-
 ### Publication 03
+
 - **DOI**: [10.1016/j.dib.2024.110852](10.1016/j.dib.2024.110852)
 - **Summary**: Extended version of CloudSEN12. We include 2000 x 2000 patches to the dataset.
 - **BibTeX Citation**:
@@ -157,7 +248,8 @@ The dataset contains four classes: clear, thick cloud, thin cloud, and cloud sha
 ## 🌈 Optical Bands
 
 
-Spectral bands related to the sensor.
+Spectral bands related to the sensor: L1C
+
 |**Name**|**Common Name**|**Description**|**Center Wavelength**|**Full Width Half Max**|**Index**|
 | :--- | :--- | :--- | :--- | :--- | :--- |
 |B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|
@@ -173,3 +265,22 @@ Spectral bands related to the sensor.
 |B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10|
 |B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11|
 |B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12|
+
+Spectral bands related to the sensor: L2A
+
+| Band | Name         | Description                                                                                   | Center Wavelength (nm) | Bandwidth (nm) | Index |
+| :--- | :----------- | :------------------------------------------------------------------------------------------- | :--------------------- | :------------ | :---- |
+| B01  | Coastal aerosol | Band 1 - Coastal aerosol - 60m                                                             | 443.5                 | 17.0          | 0     |
+| B02  | Blue          | Band 2 - Blue - 10m                                                                          | 496.5                 | 53.0          | 1     |
+| B03  | Green         | Band 3 - Green - 10m                                                                         | 560.0                 | 34.0          | 2     |
+| B04  | Red           | Band 4 - Red - 10m                                                                           | 664.5                 | 29.0          | 3     |
+| B05  | Red edge 1    | Band 5 - Vegetation red edge 1 - 20m                                                         | 704.5                 | 13.0          | 4     |
+| B06  | Red edge 2    | Band 6 - Vegetation red edge 2 - 20m                                                         | 740.5                 | 13.0          | 5     |
+| B07  | Red edge 3    | Band 7 - Vegetation red edge 3 - 20m                                                         | 783.0                 | 18.0          | 6     |
+| B08  | NIR           | Band 8 - Near infrared - 10m                                                                 | 840.0                 | 114.0         | 7     |
+| B8A  | Red edge 4    | Band 8A - Vegetation red edge 4 - 20m                                                        | 864.5                 | 19.0          | 8     |
+| B09  | Water vapor   | Band 9 - Water vapor - 60m                                                                   | 945.0                 | 18.0          | 9     |
+| B11  | SWIR 1        | Band 11 - Shortwave infrared 1 - 20m                                                         | 1613.5                | 89.0          | 10    |
+| B12  | SWIR 2        | Band 12 - Shortwave infrared 2 - 20m                                                         | 2199.5                | 173.0         | 11    |
+| AOT  | -             | Aerosol Optical Thickness                                                                    | -                     | -             | 12    |
+| WVP  | -             | Water Vapor Pressure. The height the water would occupy if the vapor were condensed into liquid and spread evenly across the column | -                     | -             | 13    |