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87 results for “sky images”
Sky images recorded during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Scattered sunlight measurements can be strongly affected by clouds, mainly due to multiple scattering effects that give rise to large uncertainties in the light path retrieval. In order to estimate cloud cover, we recorded images of the sky every 5 minutes to evaluate a cloud index, from 0 (clear sky) to 10 (completely overcast).</p> <p>This dataset presents the sky images in PNG (Portable Network Graphics) format recorded on board the R/V Akademik Tryoshnikov during the austral summer of 2016/17 as part of the circumnavigation expedition (ACE). Data coverage is from December 2016 until April 2017.</p> <p>These images form a supporting dataset to optical spectroscopy data (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p>The ship’s position can be matched with these images using the corrected cruise track (Thomas and Pina Estany, 2019; DOI: 10.5281/zenodo.3483166) or the GPS data provided with the spectroscopy data set (Benavent et al., 2020; DOI 10.5281/zenodo.3827443).</p> <p><strong>Dataset contents</strong></p> <ul> <li>YYYY-MM-DD_hh.mm.ss.png, data file, portable network graphics</li> <li>README.txt, metadata, text</li> </ul> <p>where YYYY-MM-DD_hh.mm.ss is the date and time at which the file was saved in UTC.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw optical sky images from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Comprehensive Mini-Database of the Northern Hemisphere's Winter Sky: 100 Raw Images from Ensenada, Mexico
<p>We carried out several test sessions for data collection to adjust the settings of our optical system. From October 2022 to June 2023, we executed numerous sessions to assemble our primary catalog, capturing an extensive array of sky views. A total of 100 sky observations were recorded from various directions without restrictions. These sessions were held at the peak of a hill where CICESE, our research institute, is situated at coordinates 31°52′21.5′′ N 116°40′11.8′′ W in Ensenada, Baja California, Mexico. This location was chosen because it is relatively free from urban light pollution and noise, despite its proximity to the city outskirts. This position minimizes city light interference on one side, slightly reducing light pollution, although image quality was occasionally compromised by the light pollution and facility lighting.</p> <p>Using the ASI Studio software, we captured high-resolution images of 5496 × 3672 pixels without employing pixel binning to achieve the highest possible resolution. The camera's settings were adjusted to an exposure time of 0.5 seconds and standard gain, with the lens focused at infinity and an aperture set at f/4. This setup enabled us to detect significant background noise and numerous areas that could potentially contain stars.</p> <p>More information about the article is in the:</p> <p><a href="https://doi.org/10.3390/aerospace10090748">https://doi.org/10.3390/aerospace10090748</a></p>
Image of sky region around Crab obtained by JEM-X1 instrument in the energy range 3-20 keV.
<p>https://www.astro.unige.ch/cdci/astrooda_?DEC=22.0145&E1_keV=3&E2_keV=20&RA=83.633083333333&T1=2003-03-15T23%3A27%3A40.0&T2=2019-03-16T00%3A03%3A15.0&T_format=isot&detection_threshold=20&instrument=jemx&jemx_num=1&osa_version=OSA10.2&product_type=jemx_image&query_status=new&query_type=Real&radius=5&src_name=Crab&use_scws=no</p> <p> </p>
Data used for the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data"
<p>Data used to obtain the results presented in the article "Hybrid intrahour DNI forecast model based on DNI measurements and sky-imaging data".</p> <ul> <li>CNRS_PROMES_DNI_2020-09-03_2021-01-11.zip : contains DNI measurements taken at PROMES-CNRS laboratory in Odeillo.</li> <li>The other zipped files contain image data taken at PROMES-CNRS laboratory in Odeillo. Each zipped file contains all images for one day (the date is given in the file name).</li> </ul> <p>Images and GHI measures from the following days have been used for training and cross-validation:</p> <ol> <li>2020-09-11</li> <li>2020-09-12</li> <li>2020-09-16</li> <li>2020-09-19</li> <li>2020-09-20</li> <li>2020-09-21</li> <li>2020-09-22</li> <li>2020-09-23</li> <li>2020-09-24</li> <li>2020-09-29</li> <li>2020-10-01</li> </ol> <p>Images and GHI measures from the following days have been used for test:</p> <ol> <li>2020-10-04</li> <li>2020-10-05</li> <li>2020-10-08</li> <li>2020-11-05</li> <li>2020-11-15</li> </ol>
The Longyearbyen all-sky camera full resolution image data (movie and four ASC data plots) used in the paper entitled "Auroral Morphological Changes to the Formation of Auroral Spiral during the Late Substorm Recovery Phase: Polar UVI and Ground All-Sky Camera Observations"
<p>The uploaded movie is an animation of the Longyearbyen all-sky camera (ASC) full resolution image data from 20:00 UT to 22:00 UT, which is including all ASC snapshots used in Figure 2. This movie file is the same as Movie S1. </p> <p>The uploaded four png files are the Longyearbyen all-sky camera (ASC) full resolution image snapshots, which were used in Figure S3.</p> <p>The numerical ASCII data to make the four ASC full resolution image snapshots are also uploaded; the count number of data detected by the ASC and associated latitude and longitude information in geographical coordinates of the ASC field of view.</p>
Classifications of auroral phenomena in THEMIS All-Sky images obtained via self-supervised learning
Open the record for dataset details and reuse information.
Girasol, a sky imaging and global solar irradiance dataset
<p>The energy available in Micro Grid (MG) that is powered by solar energy is tightly related to the weather conditions in the moment of generation. Very short-term forecast of solar irradiance provides the MG with the capability of automatically controlling the dispatch of energy. We propose to achieve this using a data acquisition systems (DAQ) that simultaneously records sky imaging and Global Solar Irradiance (GSI) measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed as the <em>Girasol Machine</em> (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on an outdoor tracker. The tracker updates its pan an tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal support next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.</p>
Irradiance measurements, satellite images, and clear-sky expectations for Spring 2014 in Tucson, AZ.
<p>This dataset contains the metadata and measurements from a number of irradiance sensors and rooftop solar power systems in addition to clear-sky expectations for those sensors. Irradiance derived from images from the GOES-W satellite are also included. The dataset covers April, May, and June 2014 over the Tucson, AZ, area. The data is in a number of formats including CSV, plain text, HDF5, and netCDF4. </p> <p> </p> <p>See the following for a description of the NREL calibrated GHI sensor:</p> <p>Andreas, A.; Wilcox, S.; (2010). Observed Atmospheric and Solar Information System (OASIS); Tucson, Arizona (Data); NREL Report No. DA-5500-56494. http://dx.doi.org/10.5439/1052226</p> <p> </p> <p>More information about the custom irradiance and rooftop PV sensors can be found in:</p> <p>A. T. Lorenzo, W. F. Holmgren, M. Leuthold, C. K. Kim, A. D. Cronin, and E. A. Betterton, “Short-term PV power forecasts based on a real-time irradiance monitoring network,” in <em>2014 IEEE 40th Photovoltaic Specialist Conference (PVSC)</em>, 2014, pp. 0075–0079.</p>
Movie created from all-sky 630 nm nightglow images during 162958-194158 UT on 16 April 2012
<p>Movie created from all-sky 630 nm nightglow images showing the gravity wave activity and the evolution of depletion DP1 and DP2 during 162958-194158 UT on 16 April 2012</p>
All-sky camera images from Kjell Henriksen Observatory (Svalbard) on January 7th, 2016 05:45-07:30 UT
<p>This dataset contains the images from the Sony A7S All-Sky camera located at the Kjell Henriksen Observatory operated by the University Centre in Svalbard (UNIS) on 2016-01-07 05:45-07:30 UT. The camera provides all-sky color images. The images files are in the jpg format.</p>
Conjugate observation data between DMSP satellites and all-sky imager of Chinese Yellow River station at Ny-Ålesund, Svalbard from January 2005 to December 2009
<p>Chinese Arctic Yellow River Station (YRS) locates at Ny-Ålesund, Svalbard with the geographic coordinates (78.92°N, 11.93°E) and the corrected geomagnetic latitude 76.24°. The relation between local time and universal time is MLT≈UT+3hr. In November 2003, a set of monochromatic auroral observation system was installed at YRS, which is consisted of three identical all-sky imageries (ASIs) with the filters at 427.8nm, 557.7nm and 630.0nm, respectively. DMSP (Defense Meteorological Satellite Program) satellites are a collection of polar-orbit weather satellites launched by the U.S. department of defense. This series of satellites is sun synchronous satellite, which takes about 101 minutes to orbit the earth, has an altitude of about 835-850km, an inclination of about 96°, and crosses the equatorial plane daily from south to north (ascending segment) and from north to south (descending segment) at a fixed time (DMSP F13 is at 05:45LT and 17:45LT, and F15 is at 09:30LT and 21:30LT). DMSP satellite is equipped with Special Sensor for Particle Flux (SSJ/4), which can measure the fluxes of downgoing electrons and ions with energies between from 30eV to 30keV in 19 energy steps (34, 49, 71, 101, 150, 218, 320, 460, 670, 960 eV, and 1.4, 2.1, 3.0, 4.4, 6.5, 9.5, 14.0, 20.5, 29.5 keV), with a time resolution of 1 second. According to the orbit of the DMSP satellites and the observation of the ASIs of YRS, we obtained the conjugate observation periods of the satellite flying over the ASI.</p> <p>During the period from January 2005 to December 2009, a total of 136 conjugate observation events were obtained. Moreover, according to the morphological characteristics of discrete aurora in the all-sky image, 136 events are classified according to four typical forms of dayside discrete auroras, namely 27 events of drapery dayside corona (DDC), 24 events of radial dayside corona (RDC), 37 events of hot-spot aurora (HSA), and 48 events of arc aurora (ARC). The event list of 136 events is recorded in the “asi&dmsp@YRS03-09-v2.xlsx” file. The EPS file gives the trajectory of the DMSP satellite crossing the ASI in each conjugate observation event, while the JPG files are the corresponding all-sky images.</p>
All sky images at the Sutherland and Rio Grande Observatories
<p><span>Optical airglow emissions from all-sky-imagers (ASIs) at the Sutherland (32.37<sup>o</sup>S Glat, 20.81<sup>o</sup>E Glon, 40.73<sup>o</sup>S Mlat) and Rio Grande (53.79<sup>o</sup>S Glat, 67.75<sup>o</sup>W Glon, 40.35<sup>o</sup> S Mlat) Observatories.</span></p>
Sky image features for Folsom, CA (lat=38.642N, lon=121.148W)
<p>This file contains features derived from sky images obtained for Folsom, CA (lat=38.642N, lon=121.148W).<br> The file is in comma-separated values format.</p> <p>The file has 16 columns. The first column is the timestamp in UTC in the format yyyy-mm-dd HH:MM:SS. This is the time when the image was captured.</p> <p>The other 15 columns list values for OPERATOR(ImageData), where OPERATOR can be:</p> <ol> <li>AVG: the average of the sky image pixels</li> <li>STD: the standard deviation</li> <li>ENT: the entropy</li> </ol> <p>The ImageData can be:</p> <ol> <li>R: the red color channel</li> <li>G: the green color channel</li> <li>B: the blue color channel</li> <li>RB: the Red-to-Blue ratio R/B</li> <li>NRB: the normalized Red-to-Blue ratio (R-B)/(R+B)</li> </ol>
MACADAMIA Sloan Digital Sky Survey Asteroid Photometry, Measurements, and Images
<p>This dataset contains data from the Multi-Archive Catalog of Asteroid Detections And Measurements for Interactive Access (MACADAMIA), specifically of numbered asteroids observed serendipitously by the Sloan Digital Sky Survey (SDSS).</p> <p>photometry_archive_20201027.db is a SQLite database containing data corresponding to a search of the SDSS image archive conducted on 2020 May 1 of all numbered asteroids known at the time, which yielded 2.84 million search results, 1.98 million detections that were determined to actually be within the field of view of the identified image, and 993,777 successfully measured detections. Preview images of all detections and non-detections where the object was determined to be within the field of view of an identified image are collected in the detection_previews_*tar.gz files in this dataset, indexed by detection_id in the photometry database and listed under preview_image_file in the detection_data table.</p> <p>photometry_archive_20210927.db contains an update to the SDSS search, containing data corresponding to a search of the SDSS image archive conducted on 2021 July 2 of all numbered asteroids known at the time, which yielded 2.96 million search results, 2.06 million detections that were determined to actually be within the field of view of the identified image, and 1.02 million successfully measured detections. Previews of additional detections identified in this updated search may not be available due to data loss in a system failure in August 2023.</p> <p> </p>
SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper
<p>This dataset contains:</p> <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>
Dark Skies Clasification of ISS images.
<p>The Dark Skies Citizen Science Project is indeed related to Cities at Night, a program that uses images taken from the International Space Station (ISS) to catalog and monitor light pollution in cities around the world. The project is a collaboration between the Complutense University of Madrid and other institutions.</p> <p>Citizen scientists can participate in the project by using the Cities at Night website to classify and identify the different sources of light in the images, such as streetlights, buildings, and illuminated signs. This information helps researchers create more accurate maps of light pollution and track changes in urban lighting over time.</p> <p>The Dark Skies Citizen Science Project is a great way for people to get involved in scientific research and contribute to a better understanding of the impact of light pollution on our cities and the environment.</p>
Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics
<p><strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong></p> <p>Processed ACF level data can be provided by contacting me.</p> <p><strong>DOI of the publication:</strong></p> <p><strong>README file:</strong></p> <p>PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5</p> <p>HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit)</p> <p>ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation.</p> <p>The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)</p>
Girasol, a sky imaging and global solar irradiance dataset
Open the record for dataset details and reuse information.
All-sky imager observation data from the Chinese Yellow River Station, Ny-Ålesund, Svalbard
<p><strong>Introduction</strong></p> <p>The aurora images in this dataset are from the all-sky imagers at the Yellow River Station (YRS). And it is used in the paper Automatically sketching Auroral Skeleton Structure in All-sky Image for Measuring Aurora Arcs.</p> <p>This dataset consists of the following sections:</p> <ul> <li><strong>N20040116G.zip</strong>: 4,319 observation images as of January 16, 2004;</li> <li><strong>Raw data and reading code.zip</strong>: 8634 raw data items of December 23, 2003, and reading code;</li> <li><strong>80 imgs&annotations.zip</strong>: 80 ASI images and the corresponding manual annotations with pixel-level masks.</li> </ul> <p>In the following sections, the data source, acquisition instruments, data characteristics, preprocessing methods and naming conventions of this dataset will be introduced.</p> <p><strong>Data source:</strong></p> <p>The auroral images used in this study are ground-based observation data acquired by three-wavelength (427.8, 557.7, and 630.0 nm) all-sky imagers at YRS in Ny-Ålesund, Svalbard. The geographic coordinates of YRS are 78.92°N, 11.93°E, and the corrected geomagnetic latitude is 76.24°, MLT≈UT+3 h.</p> <p><strong>Data acquisition instruments:</strong></p> <p>Data were obtained from all-sky CCD auroral imagers. The system can record 24-hour auroral activities from October to March of the following winter each year.</p> <p><strong>Data characteristics:</strong></p> <p>The time resolution of the observation results of two consecutive frames is 10 s. The camera uses a 512×512 square pixel array, and the geographic zenith is located near the center of the field of view (FOV). The coverage diameter of the all-sky FOV is approximately 1000 km. The spatial resolution is from 1.1 km at the zenith to 36 km at the horizon at an altitude of 150 km. All aurora images are provided in .bmp image format.</p> <p><strong>Data preprocess:</strong></p> <ul> <li>Subtracting dark current and rescaling. Dark current is deemed as system noise caused by equipment. It is removed from images before further operation. Every image is then stretched with a cutoff value of 4000 and rescaled from 16 to 8 bits.</li> <li>Masking and cropping. A circle mask with a radius of 220 pixels is applied to cut off the outer regions where significant wide-angle distortion happens and may contain YRS lights. Then, the original image size of 512×512 is cropped to 440×440 pixels.</li> <li> Rotation. We rotated the images counter-clockwise by 61.11 with the north being upward.</li> </ul> <p><strong>Data naming convention:</strong></p> <p>Each auroral data has a unique file name that contains the N/S, date, band, and time information.</p> <p><em>Filename example:</em> N20031221G081212.</p> <ul> <li>N: North pole</li> <li>20031221: December 21, 2003</li> <li>G: G-band</li> <li>081212: 8:12:12 UT</li> </ul>
Unwarped 630.0 nm all-sky images from Mt. John Observatory
<p>Unwarped all-sky images assuming a height of 425 km.Images are in png and IDL sav formats.</p>
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