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2,170 results for “clouds”
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China
<p>1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for </p> <p>China is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR).</p> <p>2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and </p> <p>MYD10A1).</p> <p>3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images.</p> <p>4. Each ZIP contains NDSI data for one hydrological year (August to next July). After </p> <p>uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_</p> <p>V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. </p> <p>5. The accuracy of this collection has been well validated by the in-situ snow depth observations </p> <p>and Landsat OLI NDSI maps. The detailed information can be found in the published paper.</p> <p>6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127.</p>
FIGURE 3 in A new speciesof Myxia (Hemiptera:Auchenorrhyncha: Cixiidae) collected on palms from the Reserva Privada el Silencio de Los Angeles Cloud Forest in Costa Rica
FIGURE 3. Forewing venation of Myxia hernandezi sp. n.
Point Cloud Curb Detection Dataset
<p>This dataset contains labeled point cloud data that was captured by a car equipped with a mobile laser scanner.</p> <p>It is part of the ICMLA publication <a href="https://ieeexplore.ieee.org/document/9680221">Large-Scale Curb Extraction Based on 3D Deep Learning and Iterative Refinement Post-Processing</a></p> <p>The dataset is supposed to serve as a benchmark for automated large-scale point cloud curb detection approaches.</p> <p> </p> <p>The dataset is derived from the publicly available KITTI360 dataset published in the paper</p> <p><a href="https://ps.is.tuebingen.mpg.de/publications/xie2016cvpr">Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer</a> by Jun Xie et al.</p> <p><br> LICENSE</p> <p><a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)</a> License</p> <p>If you find this dataset useful, please cite the following publication:</p> <pre><code>@INPROCEEDINGS{9680221, author={Schmitz, Jan-Christoph and Bauer, Adrian and Kummert, Anton}, booktitle={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, title={Large-Scale Curb Extraction Based on 3D Deep Learning and Iterative Refinement Post-Processing}, year={2021}, volume={}, number={}, pages={558-563}, doi={10.1109/ICMLA52953.2021.00093}}</code></pre> <p> </p> <p>Supported by the State of North Rhine-Westphalia, Germany</p>
CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
<p><strong>Description</strong></p> <p>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:</p> <p>- clear (0%)</p> <p>- low-cloudy (1% - 25%) </p> <p>- almost clear (25% - 45%)</p> <p>- mid-cloudy (45% - 65%)</p> <p>- cloudy (65% >)</p> <p>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:</p> <ul> <li> <p>2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).</p> </li> <li> <p>2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group).</p> </li> <li> <p>5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).</p> </li> </ul> <p>For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines 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. The dataset is already available here: <strong><a href="https://shorturl.at/cgjtz">https://shorturl.at/cgjtz</a></strong>. Check out our website <strong><a href="https://cloudsen12.github.io/">https://cloudsen12.github.io/</a></strong> for examples of how to download the dataset via STAC.</p>
Point cloud data for worker safety project
<p>This three files are the scanned data for the worker safety project. The scanned location includes two urban intersections and a part of an urban highway. These three files can be opened by Autodesk Recap, which is a point cloud manipulating software. This data can be requested by contacting Dr. Semiha Ergan at semiha@nyu.edu</p>
Point Cloud (Dept. Of Engineering)
<p>These are the registered and aligned point clouds from the Engineering Department acquired by the CIT lab. There are the second floor and part of the third floor of east wing of the Baker Building and the second floor of the central wing.</p>
Point Cloud Acquired by Gocator 3210 for 3D Study
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A Flexible Cloud-based HIL Testing of Batteries for Various Electrified Vehicles
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Dataset of Thesis - Attacks on the Cloud: Unveiling Cyber Assaults on Cloud Infrastructure Through Honeypot Analysis
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LST CDR Cloud Mask Stability Dataset
<p>These data are the Bayesian and operational cloud masks for matches to cloudy-sky in-situ ceilometer data, used to assess the cloud masking stability for a LST CDR. They cover overlap periods between the ATSR-2 and AATSR, AATSR and MODIS Terra, and MODIS Terra and SLSTR-A sensors.</p>
Impact of Varying Lidar Measurement and Data Processing Techniques in evaluating Cirrus Cloud and Aerosol Direct Radiative Effects
<p>Range corrected signal of the two cirrus clouds, biomass burning and dust layers, used in the analysis carried out in the paper </p>
Liquid-Ice Mass Partitioning across the Edge of Mixed-Phase Cumulus Clouds
<p>Aircraft data used in Liquid-Ice Mass Partitioning across the Edge of Mixed-Phase Cumulus Clouds</p>
Dense ion clouds attracted by supercharged supramolecular polymers boost development of neurons
GEO Series GSE253178. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome sequencing of normal and lens clouded eyes in the Myodes rufocanus (Sundevall, 1846)
GEO Series GSE231966. Myodes rufocanus. 2 samples. Type: Expression profiling by high throughput sequencing.
CALIPSO Lidar L2 5 km Cloud Profile Data V3-02
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
CALIPSO Lidar L2 1 km Cloud Layer Data V3-01
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
CALIPSO Lidar L2 1 km Cloud Layer Data V2-01
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international 'A-Train' (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.
CALIPSO Lidar L2 5 km Cloud Profile Data V2-01
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
CALIPSO Lidar L2 1 km Cloud Layer Data V1-20
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.