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108 results for “satellite imagery”
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental clean) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental clean) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and continental clean aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.</p>
Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery
<p>Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery</p> <p>LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and desert aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.</p>
Waikato Multi Labelled Satellite Imagery Dataset
<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken with using Sentinel-2 at 10m spatial resolution. Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>
BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery
<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>
Table 1 in Whales from space: Four mysticete species described using new VHR satellite imagery
<p><i>Table 1.</i> Summary of morphological characteristics per surveyed species.</p><table><tbody><tr><th></th><th>Number</th><th></th><th>Number</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th></th><td>of</td><td>Number of</td><td>of</td><td>Total</td><td>Proportion</td><td></td><td></td></tr><tr><th>Species</th><td>“definite” whales</td><td>“probable” whales</td><td>“possible” whales</td><td>number of whales</td><td>of definite whale (%)</td><td>Average body measurements (m)b</td><td>Distinctive characteristics</td></tr><tr><th>Fin whale</th><td>26</td><td>3</td><td>5</td><td>34</td><td>76.47</td><td>A: 13.49 (<i>n</i> = 9, SD = 2.92)</td><td>Streamlined</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td>body</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>B: 2.56 (<i>n</i> = 9, SD = 0.47)</td><td></td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>C: 1.94 (<i>n</i> = 6, SD = 0.23)</td><td></td></tr><tr><th>Southern right</th><td>23</td><td>12</td><td>23 (1)a</td><td>59</td><td>38.98</td><td>D: 3.68 (<i>n</i> = 1, SD = NA) A: 10.47 (<i>n</i> = 6, SD = 2.69)</td><td>White callosities</td></tr><tr><th>whale</th><td></td><td></td><td></td><td></td><td></td><td>B: 3.08 (<i>n</i> = 6, SD = 0.39)</td><td>on the head</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>C: NA (<i>n</i> = 0, SD = NA)</td><td></td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>D: 4.45 (<i>n</i> = 1, SD = NA)</td><td></td></tr><tr><th>Humpback</th><td>20</td><td>11</td><td>25</td><td>56</td><td>35.71</td><td>A: 10.62 (<i>n</i> = 5, SD = 1.36)</td><td>Long flippers</td></tr><tr><th>whale</th><td></td><td></td><td></td><td></td><td></td><td>B: 2.94 (<i>n</i> = 4, SD = 0.43)</td><td></td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>C: 2.39 (<i>n</i> = 4, SD = 0.53)</td><td></td></tr><tr><th>Gray whale</th><td>27 (2)a</td><td>18 (4)a</td><td>17 (2)a</td><td>62</td><td>43.55</td><td>D: NA (<i>n</i> = 0, SD = NA) A: 12.58 (<i>n</i> = 10, SD = 0.95)</td><td>Pale, whitish</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>B: 2.90 (<i>n</i> = 9, SD = 0.44)</td><td>body</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>C: 1.90 (<i>n</i> = 3, SD = 0.27)</td><td></td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>D: 3.06 (<i>n</i> = 8, SD = 0.26)</td><td></td></tr></tbody></table><p><sup>a</sup> Number of calves.</p><p><sup>b</sup> A: body length, B: body width, C: flipper length, D: fluke width.</p>
Table 2 in Whales from space: Four mysticete species described using new VHR satellite imagery
<p><i>Table 2.</i> Catalog of the different surface water disturbances and near surface disturbances associated with the four candidate whale species. All images are pan-sharpened. In the images where more than one sign are present, a red circle highlight the sign being referred to.</p><table><tbody><tr><th>Sign</th><th>Description</th><th>Fin whale</th><th>Southern right whale</th><th>Humpback whale</th><th>Gray whale</th></tr></tbody><tbody><tr><th>After-breach</th><td>Large white area left after a whale breached, or lobtailed, flipper-slapped</td><td>Not observed on the studied satellite images</td><td>Not observed on the studied satellite images</td><td></td><td>Not observed on the studied satellite images</td></tr><tr><th>Blow</th><td>Vaporous whitish patch next to a whale, similar looking to fog</td><td>Not observed on the studied satellite images</td><td></td><td></td><td></td></tr><tr><th>Contour</th><td>White line separating the part of the whale body that is above and below the sea surface (<i>e.g.</i>, when a whale is rolling its back or surfacing to breathe)</td><td></td><td></td><td></td><td></td></tr></tbody></table>
Satellite Imagery Dataset - Runway and Soccer Field
<p>Satellite Imagery (Google) Dataset of Runways and Soccer Fields</p> <p> </p> <p>Papers which used the dataset</p> <ul> <li><strong>A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery</strong> (<a href="https://jkreuz.github.io/publications/papers/JCruz2015_JCIS.pdf">pdf</a>)<br>JEC Cruz, EH Shiguemori, LNF Guimarães<br>Journal of Computational Interdisciplinary Sciences, 2015</li> </ul> <ul> <li><strong>Concrete and asphalt runway detection in high resolution images using LBP cascade classifier</strong> (<a href="http://plutao.sid.inpe.br/col/sid.inpe.br/plutao/2013/12.12.17.25.22/doc/Cruz_concrete.pdf">pdf</a>)(<a href="https://ieeexplore.ieee.org/abstract/document/6855892">pdf</a>)<br>JEC Cruz, EH Shiguemori, LNF Guimarães<br>BRICS Congress on Computational Intelligence, 2013</li> </ul> <p> </p> <p>more information can be found in <a href="https://jkreuz.github.io/publications/">https://jkreuz.github.io/publications/</a></p>
Landslide mapping using satellite imagery and machine learning algorithms
<p>Cyclone Idai made landfall on 15th March near Beira, Mozambique, and caused heavy rainfall across Mozambique, Malawi, Madagascar, and eastern Zimbabwe. Chimanimani District of Zimbabwe received 200 to 400 mm rainfall between 15th and 19th March, which caused widespread flooding and triggered thousands of landslides. This study aims to map the landslides in Chimanimani District and differentiate concurrent flooding from the landslides using high resolution PlanetScope imagery and DEM. Three machine learning algorithms namely, Random Forest, Artificial Neural Network, and Support Vector Machine have been deployed for the supervised landslide classification. </p> <p> </p>
Detection of slow-moving landslides from Sentinel-2 optical satellite imagery
<p>Datasets and code associated with recent ESPL publication</p> <p>"Detection of slow-moving landslides through automated satellite monitoring of surface deformation"</p> <p>The three imagery folders are zipped for convenience. The full code is included in the one .m file.</p> <p>Please get in touch with any questions.</p>
Locating Manning-based Satellite Gauging Reach for River Discharge Estimation from Remotely Sensed Imagery
<p>River discharge is critical for understanding river hydrological condition and water resource management. With the advance of earth observation technologies, estimating river discharge through remote sensing using Manning's Equation has become more and more popular for filling the gaps in gauging observations. However, finding a Manning-based Satellite Gauging Reach (MSGR) that can successfully transfer satellite signals into river discharge based on the Equation is not easy and lacks proper guidance. Theredore, we provide a practical approach for locating MSGR.<br> The manuscript is currently under review by Water Resources Research.<br> This repositry is the data for reproducing the figures in our manuscript.</p> <p>This repositry consists of four part. They are the corresponding data and results at MSGR, CR_FSE, CR_SCT locations, and elevation values along river centerline for calculating riverbed slope.</p> <p>MSGR: Manning-based Satellite Gauging Reach<br> CR_FSE: comparison reach with unsatisfactory FSE<br> CR_SCT: comparison reach with unsatisfactory SCT<br> FSE: Fluctuation degree of surface water extent<br> SCT: Stability of Channel Terrain</p>
Satellite and LiDAR imagery for canopy height and NDVI estimation for mangroves in Puerto Rico
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Data from: Modeling avian biodiversity using raw, unclassified satellite imagery
Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.
GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery
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Yield data from field measurements and satellite imagery from Sentinel-2 for three consecutive years
<p>Data From: Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F. Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques. <em>Remote Sensing</em>. 2019; 11(23):2873. https://doi.org/10.3390/rs11232873</p> <ul> <li>Yield values as point data</li> <li>Interpolated kriging yield data</li> <li>Satellite imagery</li> </ul>
Datasets from: "Deep learning for detecting and characterizing oil and gas well pads in satellite imagery"
<p>This repository contains the following: </p> <p>- <code>training</code> folder: Datasets used to train well pad and storage tank models. Note that <code>annotations_image</code> for each example are with respect to a image downloaded from the Google Earth satellite basemap at zoom level 1600 and EPSG:3857, with sizes 640x640 px and 512x512 px for well pads and storage tanks respectively (see also the <code>image_extent </code>column). <code>annotations_latlon</code> also provides the annotations in coordinate space. We also note that we are unable to redistribute the satellite imagery used to train the models in this study due to data licensing. Samples of satellite images may be made available upon request to the corresponding author.</p> <p>- <code>deployment</code> folder: Deployment detections for well pads and storage tanks across the entire Permian and Denver basins. Datasets contain confidence scores (<code>bbox_score</code>) and coordinate locations (<code>geometry</code>) for each detection, as well as a well pad identifier (<code>wp_id</code>) that indicates which well pad a storage tank belongs to.</p>
Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
<p>Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the groundwater. Many of these locations are unknown, preventing the wells from being plugged and their polluting effects averted. Remote sensing is a relatively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scale dataset for this problem, leveraging medium-resolution multi-spectral satellite imagery from Planet Labs. Our curated dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, a region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of computer vision approaches but also significant room for improvement.</p>
RBOD: An annotated satellite imagery dataset for automated river barrier object detection
<h3>Introduction:</h3> <p>Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on the locations and types of river barriers, particularly small barriers such as weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD), making it the first publicly available dataset specifically for river barrier object detection.</p> <p>The RBOD dataset comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs). In this dataset, river barriers can be classified into five classes: dams, groynes, locks, sluices, and weirs. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, namely YOLOV8-OBB, Oriented R-CNN, Rotated Faster R-CNN, R3Det, and Rotated RetinaNet, which provide performance benchmarks for future applications.</p> <h3><span>Usage Notes:</span></h3> <p>The RBOD dataset consists of three folders (namely, '<em>images</em>', '<em>labels_voc</em>', and '<em>labels_yolo</em>') and a .txt file named '<em>class</em>':</p> <p>·'<em>images</em>' folder - contains 4872 satellite images (.jpg).</p> <p>·'<em>labels_voc</em>' folder - contains 11,741 .xml files for annotations in PASCAL VOC format. In these .xml files, the position of OBB is represented as (cx, cy, width, height, angle), where 'cx' and 'cy' denote the center coordinates, 'width' and 'height' are the lengths along the x- and y-axes, and 'angle' is the clockwise rotation angle relative to the x-axis.</p> <p>·'<em>labels_yolo</em>' folder - contains 11,741 .txt files for annotations in YOLO format. In these .txt files, the OBB is represented as (class_index, x1, y1, x2, y2, x3, y3, x4, y4), where ‘class_index’ denotes the target category, and ‘x1, y1, x2, y2, x3, y3, x4, y4’ are the normalized coordinates of the four corners of the bounding box.</p> <p>·'<em>class</em>' txt file - record the classifications of river barriers and their indices, which correspond to the ‘class_index’.</p> <p>Note that each folder splits into three subfolders: train (70%), test (20%), and val (10%).</p>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Datasets supporting the publication: Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador
<p>##################################</p> <p><strong>Datasets supporting the publication:</strong></p> <p><em>Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador.</em></p> <p>International Journal of Remote Sensing. <a href="https://doi.org/10.1080/01431161.2023.2205983">https://doi.org/10.1080/01431161.2023.2205983</a></p> <p>C. Scott Watson<sup>a</sup>*, John R. Elliott<sup>a</sup>, Marco Córdova<sup>b</sup>, Jonathan Menoscal<sup>b</sup>, Santiago Bonilla-Bedoya<sup>c</sup></p> <p><em><sup>a</sup>COMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK</em></p> <p><em><sup>b</sup>Facultad Latinoamericana de Ciencias Sociales, FLACSO, Quito, Ecuador</em></p> <p><em><sup>c</sup>Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica,Machala y Sabanilla, 170301, Quito, Ecuador</em></p> <p><strong>-Please refer to the publication for details on the production of each dataset.<br> -Please cite the publication and this dataset repository when using the data.</strong></p> <p>##################################</p> <p><strong>Data:</strong></p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>J1_mosaic_max.tif</td> <td>Mosaicked Jilin-1 multi-spectral night-time image of Quito, Ecuador. Acquisition: 8th July 2021 at ~10:30 UTC (05:30 local time)</td> </tr> <tr> <td>corine_landcover_S2_20210705T153621_20210705T154215_T17MQV.tif</td> <td>Land cover classification applied to a Sentinel-2 image (5th July 2021)</td> <td> </td> </tr> <tr> <td>corine_landcover_symbology_qgis.txt</td> <td>Land cover classification symbology for QGIS</td> </tr> <tr> <td>light_type_classification.tif</td> <td>Light type classification: class 1 = LED, class 10 = HPS.</td> </tr> <tr> <td>classified_light_locations.shp</td> <td>Classified light source (point) locations</td> </tr> </tbody> </table>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
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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.