This dataset follows the TACO specification.

  cloudsen12plus

Website: https://cloudsen12.github.io/ version: 1.1.2 The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2 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… See the full description on the dataset page: https://huggingface.co/datasets/tacofoundation/cloudsen12.

🚨 New Dataset Version Released! We are excited to announce the release of Version [1.1] of our dataset! This update includes: [L2A & L1C support]. [Temporal support]. [Check the data without downloading (Cloud-optimized properties)]. 📥 Go to: https://huggingface.co/datasets/tacofoundation/cloudsen12 and follow the instructions in colab

  CloudSEN12 NOLABEL





  A Benchmark Dataset for Cloud Semantic Understanding

CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic… See the full description on the dataset page: https://huggingface.co/datasets/csaybar/CloudSEN12-nolabel.

CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:- clear (0%)- low-cloudy (1% - 25%) - almost clear (25% - 45%)- mid-cloudy (45% - 65%)- cloudy (65% >)An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).2000 ROIs with scribble-level annotation, where the annotation time is 15 minutes (scribble group).5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, combining human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no-annotation dataset is generated automatically, with manual annotation only in the clear image patch. A backup of the dataset in STAC format is available here: https://shorturl.at/cgjtz. Check out our website https://cloudsen12.github.io/ for examples.

Detecting and screening clouds is the first step in any optical remote sensing (RS) analysis. Cloud formation is diverse, presenting many shapes, thicknesses, and altitudes. This variety poses a significant challenge to developing effective cloud detection algorithms since most datasets shortfall an unbiased representation. To address this issue, we have built CloudSEN12+, a significant expansion of the CloudSEN12 dataset. This new dataset doubles the expert-labeled pixels, making it the largest cloud detection dataset for Sentinel-2 imagery up to date. We have carefully reviewed and refined previous human labels in this new release to ensure maximum trustworthiness. We hope CloudSEN12+ will be a valuable resource for the cloud detection research community.

aialliance/cloudsen12 dataset hosted on Hugging Face and contributed by the HF Datasets community

cloudsen12

A dataset about clouds from Sentinel-2 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… See the full description on the dataset page: https://huggingface.co/datasets/jfloresf/mlstac-demo.

Dataset Card for Copernicus-Bench

A hierarchical ML benchmark for Copernicus Sentinels, with 15 datasets spread into three task levels covering all major Sentinel missions (S1,2,3,5P). (Officially named "Copernicus-Bench", initially named "SentinelBench")

  Dataset Details

Level Name Modality Task

Images

Image Size

Classes

Source License

L1 Cloud-S2 S2 TOA segmentation (cloud) 1699/567/551 512x512x13 4 CloudSEN12 CC 0 1.0

L1 Cloud-S3 S3 OLCI… See the full description on the dataset page: https://huggingface.co/datasets/wangyi111/Copernicus-Bench.