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108 results for “satellite imagery”
Fig. 3 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 3. Negative and positive anomalies (white and black bars) of the Mean Annual Temperature time series for the period of 1985–2018 (data from the meteorological station Sofia)
Fig. 2 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 2. Changes in the number of grazing livestock in the southern central Bulgarian planning region for the period 2001–2018
Figure 4 in Whales from space: Four mysticete species described using new VHR satellite imagery
Figure 4. Radiance values of each candidate species compared to the radiance values of sea water of three of the four study locations. For clarity reasons, the waters off Maui Nui are not represented in this figure as their radiance values are fully overlapping with Península Valdés. The shaded areas around the dotted lines correspond to the standard error of the mean.
Figure 3 in Whales from space: Four mysticete species described using new VHR satellite imagery
Figure 3. Radiance values of the four studied species for four multispectral bands. The shaded areas around the dotted lines correspond to the standard error of the mean.
Figure 6 in Whales from space: Four mysticete species described using new VHR satellite imagery
Figure 6. Radiance values of gray, fin, and humpback whales compared to the radiance values of nonwhale objects. (A) In the image of Laguna San Ignacio, boats were the only observed, nonwhale object. Graph (B) are the results for the Pelagos Sanctuary image and (C) for the image of Maui Nui. The shaded areas around the dotted lines correspond to the standard error of the mean.
Figure 2 in Whales from space: Four mysticete species described using new VHR satellite imagery
Figure 2. Pan-sharpened WorldView-3 satellite images of four "definite" gray whales in Laguna San Ignacio (top left), a "definite" fin whale in the Pelagos Sanctuary (top right), two "definite" humpback whales in Maui Nui (bottom left), and a "definite" southern right whale in Península Valdés (bottom right).
Figure 1 in Whales from space: Four mysticete species described using new VHR satellite imagery
Figure 1. Locations of study areas: (1) Maui Nui in the United States of America, (2) Laguna San Ignacio in Mexico, (3) Pelagos Sanctuary in the Ligurian Sea, and (4) Península Valdés in Argentina. Black shapes in the four subareas represent the extent of the satellite imagery acquired and used in this study.
Look Up Tables for removing background atmospherical signal in visible satellite imagery
<p>Look Up Tables for removing the background atmospherical signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols in satellite imagery utilising channels in the visible spectral range</p> <p>Derived from LibRadTran simulations for various standard atmospheres and various aerosol profiles.</p>
Sentinel-1 Satellite Imagery Based Ice Road Detection and Monitoring
<p>Canada’s northern ice roads in winter which is more than 3300 miles are freezing later and melting earlier, drastically reducing the forecasting capabilities for the safe use. An attractive Area of Interest (AoI) could be around the region “Yellowknife” in Canada, which is also mentioned in the recent TV show (<a href="https://en.wikipedia.org/wiki/Ice_Road_Truckers">https://en.wikipedia.org/wiki/Ice_Road_Truckers</a>). This area could be used to train the algorithm to detect the current situation and to forecast the time window to close and open the ice tracks to the citizens. Mockup could be achieved by building a screenshot mockup on mobile device (Smartphone/Tablet).</p>
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
<p>A <a href="https://www.sciencedirect.com/topics/engineering/photovoltaics">photovoltaic</a> (PV) dataset from satellite and aerial imagery. The dataset includes three groups of PV samples collected at the spatial resolution of 0.8m, 0.3m and 0.1m, namely PV08 from Gaofen-2 and Beijing-2 imagery, PV03 from aerial photography, and PV01 from UAV orthophotos. PV08 contains rooftop and ground PV samples. Ground samples in PV03 are divided into five categories according to their background land use type: shrub land, grassland, cropland, saline-alkali, and water surface. Rooftop samples in PV01 are divided into three categories according to their background roof type: flat concrete, steel tile, and brick. Data document can refer to the preprint https://essd.copernicus.org/preprints/essd-2021-270/</p>
Sentinel-3 Altimetry satellite imagery for Inland Water Altimetry Monitoring
<p>Sentinel-3 Altimetry satellite imagery for Inland Water Altimetry Monitoring</p>
Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping
<p>Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping</p>
HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection
<p>The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.</p>
INCYDE: A large scale cyclone detection and intensity estimation dataset using satellite infrared imagery
<p>INCYDE (INSAT-based Cyclone Detection and Intensity Estimation) is a cyclone detection and intensity estimation dataset. The cyclone images in the dataset are captured from INSAT 3D/3DR satellites over the Indian Ocean. The proposed INCYDE dataset contains over 100k cyclone images with augmentations taken from cyclones over the Indian Ocean from the year 2013 to 2021. The dataset pertains to two specific tasks: cyclone detection as an object detection task, and intensity estimation as a regression task. In addition to the dataset, this study introduces baseline models that were trained on the newly presented dataset</p>
SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery
<p>This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark</p>
Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites
Open the record for dataset details and reuse information.
Abstractions of all intersections in Australia based on satellite imagery
<p>This dataset contains processed satellite imagery of 898,418 intersections in Australia. Imagery has been processed using computer vision techniques to emphasise features important for road safety.</p> <p>Detailed information is available in the corresponding journal article:</p> <p>Wijnands J.S., Zhao H., Nice K.A.,Thompson J., Scully K., Guo J., Stevenson M., Identifying safe intersection design through unsupervised feature extraction from satellite imagery. <em>Computer-Aided Civil and Infrastructure Engineering</em>, 2020.</p> <p> </p>
SlumMapVisionBR: A Dataset for Mapping Slums with Medium-Resolution Satellite Imagery
<p>This dataset contains Landsat images (30m/pixel) and masks with slum locations in Brazil. </p><p>This imagery can be used to train models that help keep track of Goal 11.1 of the United Nations Sustainable Development Goals. </p><p>Details on how the data was collected and labelled are available at: <a href="https://github.com/ml-labs-crt/SlumMapVisionBR">https://github.com/ml-labs-crt/SlumMapVisionBR</a>, along with the code to generate the dataset and benchmark results.</p><p>Description of files:</p><ul><li>images.zip contains the images.</li><li>masks.zip contains the masks with slum locations (1 indicates the area has been labelled as a slum).</li><li>metadata.csv contains information about the 116 locations in this dataset.</li><li>metadata_dictionary.txt includes a description of the fields in metadata.csv.</li></ul><p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data more straightforward for the community. Please get in touch with us by <a href="https://github.com/ml-labs-crt/SlumMapVisionBR/issues">posting an issue on GitHub</a>.</p>
Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting"
<p>The file uploaded here is an animation that visually illustrates the outputs of the a newly developed machine learning based FLS (<strong>F</strong>og and <strong>L</strong>ow <strong>S</strong>tratus) detection algorithm for the SEVIRI (<strong>S</strong>pinning <strong>E</strong>nhanced <strong>V</strong>isible and <strong>I</strong>nfra<strong>R</strong>ed <strong>I</strong>mager) instrument onboard the MSG (<strong>M</strong>eteosat <strong>S</strong>econd <strong>G</strong>eneration) geo-stationary satellites over the 24hr cycle of the day for the day of <strong>02/March/2021</strong> and compares them with the corresponding raw channel values observed by SEVIRI. The proposed algorithm classifies each SEVIRI pixel as "clear-sky", "FLS", or "non-FLS-cloud" (identified with Khaki, Red, and Blue in the animation) based on the SEVIRI pixel values of BT12.0, BT8.7 - BT12.0, BT10.8 - BT12.0, and BT12.0 - BT13.4 plus the standard deviation of each of these variables in a spatial window sized 3x3 pixels with the central pixel being the target pixel. </p><p><br>In this animation, the left-hand panel shows a false-color RGB image constructed based on the SEVIRI raw channel data with the red, green, and blue channels being BT12.0- BT13.4, BT8.7 - BT12.0, and BT10.8 - BT12.0, respectively. In this panel, the green color represents the high clouds, and the light and dark red colors represent the clear-sky and FLS, respectively. The right-hand panel of this animation also shows the outputs of the ML FLS detection algorithm developed in the present study.</p>
Data files for Sheehan et al. 2023 'City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona' DOI: https://doi.org/10.3390/rs15245709
<p>Data files for Sheehan et al. (2023) City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona. Remote Sensing. 15(24) DOI: <a href="https://doi.org/10.3390/rs15245709">https://doi.org/10.3390/rs15245709</a></p> <p>Description of contents: </p> <p>xView-YOLOv3_Model6_Barcelona_weights.pt</p> <p>This file contains the pre-trained weights for the xView-YOLOv3 model (model code available here: https://github.com/ultralytics/xview-yolov3). These weights were trained on a manually created training data set of vehicles present in WorldView 2/3 imagery covering the city of Barcelona. The weights relate to Model 6 set up: a single vehicle class (parked, static and moving), RGB imagery, Barcelona training data set derived anchor boxes, 1500 x 1500 pixel sized images and to 1000 epochs. </p> <p> </p>
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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.