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36 results for “solar images”
SunspotsYoloDataset: annotated solar images captured with smart telescopes (January 2023 - May 2024)
<p><strong>SunspotsYoloDataset</strong> is a set of 1690+380+128 high-resolution RGB astronomical images captured with smart telescopes with specific solar filters and annotated with the positions of sunspots that are effectively in the images. Two instruments were used for several months from Luxembourg and France between January 2023 and May 2024: a Stellina smart telescope (<a href="https://vaonis.com/stellina">https://vaonis.com/stellina</a>) and a Vespera smart telescope (<a href="https://vaonis.com/vespera">https://vaonis.com/vespera</a>).</p> <p><strong>SunspotsYoloDataset</strong> can be used to train YOLO detection models on solar images, enabling the prediction of unexpected events such as Borealis Aurora with astronomical equipment accessible to the public.</p> <p><strong>SunspotsYoloDataset</strong> is formatted with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (<a href="https://www.makesense.ai">https://www.makesense.ai</a>). More precisely, there is a ZIP file containing RGB images in JPEG format (minimal compression), and text files containing the positions of sunspots. Each RGB image has a resolution of 640 × 640 pixels.</p> <p>For more details about the dataset, please contact the author: olivier.parisot@list.lu .</p> <p>For more information about Luxembourg of Science and Technology (LIST), please consult: <a href="https://www.list.lu">https://www.list.lu</a> .</p> <p> </p>
Images of solar flares in 1600 Angstrom wavelenght
<p>This dataset contains solar flares in classes B, C, M, and X at 1600 Angstrom wavelength. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
Images of solar flares in HMI Continuum
<p>This dataset contains solar flares in classes B, C, M, and X at HMI Continuum. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
Images of solar flares in 1700 Angstrom wavelength
<p>This dataset contains solar flares in classes B, C, M, and X at 1700 Angstrom (Å) wavelength. The images were obtained from the Solar Dynamics Observatory by the Atmospheric Imaging Assembly instrument. Besides, the dataset contains labels to indicate the active regions in each image.</p>
The Data for Cloud Removal in Full-disk Solar Images Using Deep Learning
<p><em>Cloud Removal in Full-disk Solar Images Using Deep Learning. For more information, please refer to README.md.</em></p> <p><em>Please note that the data and model parameters zip is not available in github. Please download them in this file.</em></p> <p>If you need help, feel free to contact me at <a href="mailto:szh@kust.edu.cn" target="_blank" rel="noopener">szh@kust.edu.cn</a> or <a href="mailto:dp24163@163.com" target="_blank" rel="noopener">dp24163@163.com</a>.</p>
SUTO-Solar through-turbulence open image dataset
<p>Database of solar small-area images disturbed by turbulent atmosphere. Together with short series of images, an MFBD-assisted recovered image is provided. Paper describing the dataset as well as utilized instrumentation can be found in the original work at https://www.mdpi.com/1424-8220/22/20/7902</p> <p> </p>
A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
<p><strong>Summary</strong></p> <p>Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, residential PV installations are deployed at an unprecedented pace, and their safe integration into the grid necessitates up-to-date, high-quality information. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata. We provide installation metadata for more than 28000 installations. We provide ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.</p> <p>This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", published in Scientific data. The article is accessible here : <a href="https://www.nature.com/articles/s41597-023-01951-4">https://www.nature.com/articles/s41597-023-01951-4</a> These complete records consist of:</p> <ol> <li>The complete training dataset containing RGB overhead imagery, segmentation masks and metadata of PV installations (folder <strong>bdappv</strong>),</li> <li>The raw crowdsourcing data, and the postprocessed data for replication and validation (folder <strong>data</strong>).</li> </ol> <p><strong>Data records</strong></p> <p>Folders are organized as follows:</p> <ul> <li><strong>bdappv/</strong> Root data folder <ul> <li><strong>google / ign:</strong> One folder for each campaign <ul> <li><strong>img/</strong>: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.</li> <li><strong>mask/</strong>: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.</li> </ul> </li> <li><em>metadata.csv</em> The <code>.csv</code> file with the installations' metadata.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>data/ </strong>Root data folder <ul> <li><strong>raw/</strong> Folder containing the raw crowdsourcing data and raw metadata; <ul> <li><em>input-google.json</em>: <code>.json </code>input data data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the first annotation campaign;</li> <li><em>input-ign.json</em>:<em> </em><code>.json </code>input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the second annotation campaign;</li> <li><em>raw-metadata.json</em>: <code>.json </code>output containing the PV systems’ metadata extracted from the BDPV database before filtering. It can be used to replicate the association between the installations and the segmentation masks, as done in the notebook metadata.</li> </ul> </li> <li><strong>replication/</strong> Folder containing the compiled data used to generate the segmentation masks; <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis.json</em>: <code>.json </code>output on the click analysis, compiling raw input into a few best-guess locations for the PV arrays. This dataset enables the replication of our annotations,</li> <li><em>polygon-analysis.json</em>: <code>.json </code>output of polygon analysis, compiling raw input into a best-guess polygon for the PV arrays.</li> </ul> </li> </ul> </li> <li><strong>validation/</strong> Folder containing the compiled data used for technical validation. <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis-thres=1.0.json</em>: <code>.json </code>output of the click analysis with a lowered threshold to analyze the effect of the threshold on image classification, as done in the notebook annotation;</li> <li><em>polygon-analysis-thres=1.0.json</em>: <code>.json </code>output of polygon analysis, with a lowered threshold to analyze the effect of the threshold on polygon annotation, as done in the notebook annotations.</li> </ul> </li> <li><em>metadata.csv</em>: the <code>.csv </code>file of filtered installations' metadata.</li> </ul> </li> </ul> </li> </ul> <p><strong>License</strong></p> <p>We extracted the thumbnails contained in the <strong>google/img/</strong> folder using Google Earth Engine API and we generated the thumbnails contained in the <strong>ign/img</strong><strong>/</strong> folder from high resolution tiles downloaded from the online IGN portal accessible here: <a href="https://geoservices.ign.fr/bdortho">https://geoservices.ign.fr/bdortho</a>. Images provided by Google are subjet to Google's terms and conditions. Images provided by the IGN are subject to an open license 2.0.</p> <p>Access the terms and conditions of Google images at this URL: <a href="https://www.google.com/intl/en/help/legalnotices_maps/">https://www.google.com/intl/en/help/legalnotices_maps/</a></p> <p>Access the terms and conditions of IGN images at this URL: <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf</a></p>
Airglow Imaging Observations of Plasma Blobs: Merging and Bifurcation During Solar Minimum over Tropical Region
<p>This is the dataset for the plasma blobs merging (26/10/2019) and bifurcations (01/03/2019) used in our research. It is the OI 630nm wavelength.</p>
Active Region Magnetograms for Solar Flare Prediction: Reduced Resolution Dataset Images
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.jq2bvq898. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the reduced resolution (224x224 pixels) images in .png format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1307 through 1505
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1307 through 1505 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1064 through 1306
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1064 through 1306 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1506 through 1707
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1506 through 1707 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1708 through 1918 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1919 through 2103
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1919 through 2103 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2284 through 2488
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2284 through 2488 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2104 through 2283
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2104 through 2283 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2489 through 2731
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2489 through 2731 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1064 through 1527
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1064 through 1527 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
SDO/AIA cutout images for 2-April-2022 solar filament eruption
<p>This entry contains sets of "cutout" images for the solar filament eruption that took place on 2-April-2022 around 13:30 UT. The images are from the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO). AIA takes full-disk images, and the cutout images were obtained from the Joint Science Operations Center (JSOC) website's cutout service.</p> <p>Tar files are available for the 304 angstrom and 131 angstrom AIA channels. </p> <p>These images were used to generate movies for a paper by M. Janvier et al. that was submitted to the journal Astronomy & Astrophysics in 2023.</p> <p> </p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 2470 through 2731
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2470 through 2731 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.