Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

Enescu, Ionuţ Iosifescu; Pernas, Lucia Espona; Haas-Artho, Dominik; Buchholz, Rebecca Kurup; Hanimann, David; Rüetschi, Marius; Karger, Dirk Nikolaus; Plattner, Gian‐Kasper; Hägeli, Martin; Ginzler, Christian; Zimmermann, Niklaus E.; Pellissier, Loïc

doi:10.3390/geomatics1030021

“…COG is a standard imagery format for web-optimized access to raster data. It has a specific internal pixel structure that allows clients to request just specified areas of a large image by submitting HTTP range requests 58 . The IP properties are shared using the SpatioTemporal Asset Catalog (STAC) specification.…”

Section: Data Recordsmentioning

confidence: 99%

CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Aybar

¹

,

Ysuhuaylas

²

,

Loja

³

et al. 2022

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Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples.

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“…COG is an imagery format for web-optimized access to raster data. It has a specific internal pixel structure that allows clients to request just specified areas of a large image by submitting HTTP range requests 56 . The IP properties are shared using the SpatioTemporal Asset Catalog (STAC) specification.…”

Section: Data Recordsmentioning

confidence: 99%

CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Aybar¹,

Ysuhuaylas²,

Loja³

et al. 2022

Preprint

View full text Add to dashboard Cite

Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples. The dataset is made publicly available at https://cloudsen12.github.io/.

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Challenges and Opportunities in the Remote Sensing Big Data

Di

¹

,

Yu

²

2023

Springer Remote Sensing/Photogrammetry

1

0

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Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

Cited by 3 publications

References 13 publications

CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Challenges and Opportunities in the Remote Sensing Big Data

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