Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
109
datasets available to search
ShareScore release 0.9.0
Dataset results
109 results for “satellite image”
In vivo timelapse imaging and analysis of Golgi satellite organelle distribution and movement in the neural progenitor cells of the brain
Open the record for dataset details and reuse information.
Project Panormos Archaeological Survey: Satellite Image (gis-copernicus)
<p>This forms part of the preliminary open data release for the Project Panormos archaeological survey.</p> <p>This "panormos/gis-copernicus" repository contains a raster used as a background for mapping the survey data. This image was derived from <a href="https://scihub.copernicus.eu">Copernicus Sentinel Data</a>, and is redistributed in compliance with the legal notice on the use of Copernicus Sentinel Data</p> <p> </p>
SENTINEL-2 SATELLITE IMAGE (2015, AUGUST 7) FOR CHANGE DETECTION ON "MURGIA ALTA" - TIME T2 DATA
<p><strong>Time T2 data:</strong> Sentinel-2 image, 10 bands at 20 meters spatial resolution; 2015, August 7; subset of the "Murgia Alta" protected area; projected in WGS84/UTM33; coregistered on the time T1 data.</p>
TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection
<p>This dataset is from the paper "TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection", which has been accepted by the WACV 2024 CV4EO Workshop.</p>
Change-Aware Sampling and Contrastive Learning for Satellite Images
<p><strong>Instructions</strong></p> <div>Change-Aware Sampling and Contrastive Learning for Satellite Images</div> <div>The 1 million sized dataset in compressed format.</div> <div>This dataset is split in 4 parts due to Zenodo's size restictions.</div> <div>Each part can be downloaded using the following link.</div> <p><strong>Part 1: </strong><a href="../records/10913216">https://zenodo.org/records/10913216</a></p> <p><strong>Part 2: </strong><a href="../records/10914902">https://zenodo.org/records/10914902</a></p> <p><strong>Part 3: </strong><a href="10915715">https://zenodo.org/uploads/10915715</a></p> <p><strong>Part 4: </strong><a href="../records/10916979">https://zenodo.org/records/10916979</a></p> <p> </p> <div>Use the following commands to combine and extract the compressed file.</div> <blockquote> <div>cat clean_1m_geography_part* > clean_1m_geography.tar.gz</div> <div>tar -xvf clean_1m_geography.tar.gz</div> </blockquote> <p> </p> <p><strong>Paper Abstract</strong></p> <p>Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithms. Self-supervised learning instead provides a way to learn effective representations for various downstream tasks without labels. In this work, we leverage characteristics unique to satellite images to learn better self-supervised features. Specifically, we use the temporal signal to contrast images with long-term and short-term differences, and we leverage the fact that satellite images do not change frequently. Using these characteristics, we formulate a new loss contrastive loss called Change-Aware Contrastive (CACo) Loss. Further, we also present a novel method of sampling different geographical regions. We show that leveraging these properties leads to better performance on diverse downstream tasks. For example, we see a 6.5% relative improvement for semantic segmentation and an 8.5% relative improvement for change detection over the best-performing baseline with our method.<br><br><br></p> <div> <div> </div> </div>
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</p>
PASTIS-R - Panoptic Segmentation of Radar and Optical Satellite image TIme Series
<p>Extension of the <a href="https://zenodo.org/record/5012942#.YaUaQ7so-V6">PASTIS benchmark</a> with radar and optical image time series.</p> <p>See associated <a href="https://arxiv.org/abs/2112.07558v1">article</a> for more details.</p>
PASTIS-R PixelSet - Radar and Optical Satellite Image Time Series in Pixel-Set format
<p>Extension of the <a href="https://zenodo.org/record/5012942#.YaUaQ7so-V6">PASTIS benchmark</a> with radar and optical image time series.<br> In this version the satellite image time series are prepared in pixel-set format.</p> <p>See associated <a href="https://arxiv.org/abs/2112.07558v1">article</a> for more details.</p>
Dataset with square plots across Sierra Nevada (Spain) where the contours of all juniper shrubs were annotated as polygons using centimetric GPS and very high resolution aerial and satellite RGB images
<p><strong>This dataset is a shapefile of 767 polygons describing the contours of Juniperus communis L. and Juniperus sabina L. shrubs for the year 2021 in rectangular plots across Sierra Nevada. The coordinates of the polygons were obtained from a field work campaign with a differential centimetric GPS, and their contours were drawn manually in QGIS using the Google Earth satellite image for 2020 and the PNOA aerial image for the 2020. </strong></p> <p><strong>This dataset also contains an excel file describing the features of each polygon: the polygon centroid coordinates, the type of species, the sexgender, the morphotype, the damage in the vegetation cover estimated in the field and telematically, certainty of the digitalization with QGIS and also if the differential centimetric GPS used belongs to the University of Granada or the University of Almeria. </strong></p>
Impact crater identification results for Chang'e-4 satellite image area and landing camera image area
<p>Here are the results of impact crater identification from Chang'e-4 satellite image data and Chang'e-4 landing camera image data.</p> <p>There are 99 impact craters identified in the landing camera, with diameters ranging from 1.55m to 50.87m.There are 947 impact craters identified in the satellite images, with diameters ranging from 48.37m to 2,222.34m in diameter.</p> <p>All impact craters are categorised into five degradation classes (A, AB, B, BC, C).</p> <p>Landing camera image source: <a href="https://doi.org/10.5281/zenodo.3600427" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.3600427</a><br>Satellite image source: <span>https://darts.isas.jaxa.jp/planet/pdap/selene/index.html.en</span></p> <p> </p>
Satellite Images from ASTER TIR and Landsat 9 (RGB+TIR) for evaluation of unmixing methodologies
Open the record for dataset details and reuse information.
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods - data and weights
<p>Release for "Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods", submitted to "Atmospheric Chemistry and Physics<br><br>- Train, validation and test dataset for Lippendorf, Boxberg, and Turow CNN applications.<br>- Test dataset for OCO3 SAM application.<br>- Weights and architecture of the trained CNN.</p>
Image Satellite Semaine Innovation Recherche
<p>Darwin.rar : pour mercredi 31/10, vous devez extraire le contenu.</p> <p>Wild.rar : pour ceux qui veulent tester les algorithmes avec une autre image, vous devez extraire le contenu.</p> <p>WholeImage.rar : l'image en entier qui contient la partie Darwin et Wild, pour ceux qui veulent tester les algorithmes avec une image plus complexe qui contient plus d'elements.</p> <p> </p>
Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images
<p>Supplementary materials v2 for the article Unagaev A, Korotkova I and Efremova N. "Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images using phenological, geographic and regional statistical information"<br> </p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the U.S.
Open the record for dataset details and reuse information.
3000 satellite images of buildings
<p>This dataset contains 3000 satellite images. In the center of each image there is a building, whose roof can be visually identified. </p> <p>More information can be found on project website <a href="https://github.com/NHERI-SimCenter/BRAILS">https://github.com/NHERI-SimCenter/BRAILS</a></p> <p>This material is based upon work supported by the NSF National Science Foundation under Grant No. 1612843.</p>
Data from: Satellite image texture for the assessment of tropical anuran communities
The relationship between environmental heterogeneity and biodiversity represents a cornerstone of ecological research. While environmental descriptors over large extents usually have medium to low spatial resolution, in-situ measures provide accurate information for limited areas, and a gap remains in providing remote descriptors that represent local environmental structure. Texture from satellite images can represent fine-scale heterogeneity over wide spatial coverage, but to date, it has mostly been used to predict general aspects of species diversity, such as richness. Here, we assess the utility of image textures from high resolution satellite images (RapidEye 3A) and in-situ variables to predict differences in the composition of anuran communities in a tropical savanna (Cerrado) of Brazil. While in-situ measures accounted for compositional differences of the whole community, two measures of image textures were associated only with the variation of species within the Hylidae family (adj. R² = 0.16 and 0.14). Comparatively, image textures predicted ~2/3 of the variation explained by in-situ measures (adj. R² = 0.23). When both approaches were combined, a greater compositional variation was achieved (adj. R² = 0.28), with 1/5 of it shared by both in-situ and textures, and 1/5 attributed solely to texture. Our findings suggest that image texture can complement the assessment of environmental heterogeneity acting on the assembly of local anuran communities. This approach can be valuable for explicitly including spatial heterogeneity in biological assessments over broad spatial extents, especially for biological groups strongly filtered by environmental conditions.
ScienceDex guides
Understand access before you commit
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.