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32 results for “land use mapping”
Mapped Trees in CRUI Land Use Project at Harvard Forest 1996-2006
Numerous variables related to land use disturbance and recovery processes influence forest composition, structure, and growth. We measured forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill to test predictions about species composition, stand structure, and productivity in response to agricultural land use legacies. A permanent 30 x 50 m plot gridded in 5 x 5 m sub-plots was established in each of the 6 sites. All trees at least 2.5 cm DBH (diameter at breast height, 1.3 m) were mapped visually in the field, marked with an aluminum tag, and their DBH recorded in summer 1996. Individual boles of multi-stemmed trees were measured separately and a composite single DBH was calculated. Standing dead trees were also mapped and their DBH recorded as well. The 1996 data were used to determine above ground woody biomass using allometric equations for individual trees. The permanent plots were re-surveyed ten years later in fall 2006. The status of each tree mapped in 1996 was recorded (alive, dead standing, dead fallen, forked) and DBH’s were re-measured. Additional trees that grew across the 2.5 cm DBH threshold during the ten-year period were mapped and their DBH’s measured. Composite diameters and above ground woody biomass were determined for forked trees as in 1996. The 2006 re-measurements were used to analyze changes in species composition, stand structure (density, diameter distribution), and mortality patterns among species and size classes, and to calculate net changes in above ground woody biomass across the ten-year period.
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
HR land use maps part 2
<p>High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. These datasets are in ETRS89 in line with those that will be uploaded to the platform of the project. This v2 includes metadata</p>
Ouagadougou land use map at street block level
<p>This datatset contains a land use classification of Ouagadougou (Burkina Faso) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Ouagadougou_landuse.gpkg" : GeoPackage with two layers: (1) layer of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table. (2) layer with manual correction made by GEORGANOS Stefanos (sgeorganos@ulb.ac.be).</li> <li>"Ouagadougou_landuse_style_QGIS.zip" : Files for style for rendering in QGIS.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_UNPLA" : Probability to belong to class UNPLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes. Depending on the layer, the label is with or without manual corrections</li> </ul> <p>Legend classes label:</p> <ul> <li>"VEG" : Vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"UNPLAN" : Unplanned residential built-up</li> <li>"UNPLAN_LD" : Unplanned residential low density built-up</li> <li>"UNCERT" : Uncertain classification</li> <li>"WET" : Wetlands</li> <li>"AGRI" : Agricultural land</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Mapping ecosystem types and land cover types in the Seychelles granitic islands, using Earth Engine and Sentinel-2
<p>We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis</p> <p>The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution.</p> <p>A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.</p>
Systematic map of literature on land use conflicts: Dataset
<p>This dataset compiles the international, peer-reviewed literature on land use conflicts that was published between 2005 and 2020. It lists 306 publications and provides for each of them information regarding the research methods, research aims, covered land use types, covered geographical region, subject area, definition of "land use conflict", and conceptual approach. It can be used to identify publications on specified topics or combinations of topics, such as literature on land use conflicts in Africa regarding the land use type pastoralism. The ROSES for systematic map reports file provides information of how the systematic map was compiled and the list of unobtainable and excluded publications lists those publications that appeared in the initial database search but were not included in the mapping, with reasons for exclusion. More information on the dataset's creation, a detailed analysis, and interpretation are provided in the corresponding journal article:</p> <p>Fienitz, Meike. 2023. "Taking stock of land use conflict research: A systematic map with special focus on conceptual approaches." <em>Society & Natural Resources</em>. http://dx.doi.org/10.1080/08941920.2023.2199380</p>
HR land use maps final version
<p>High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. The .tif files of the 12 maps are uploaded with visualization files. These datasets are in WGS84 in line with those that will be uploaded to the platform of the project and also this last version (v2) includes metadata</p>
Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version
<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>
Land use maps Bono East, Ghana (2020 and 2050)
<p>Land use GIS files of the Bono East region in Ghana at a resolution of 30x30 meters. This publication contains two geo-tif files and an accompanying QGIS style. The '2020' GIS file represents the current land use. The '2050' file represents a possible future as created through the application of the iCLUE land use projection model.</p><p>For more information, see Duku et al (2023) - <a href="https://doi.org/10.18174/584095">https://doi.org/10.18174/584095</a> or <a href="https://edepot.wur.nl/584095">https://edepot.wur.nl/584095</a></p>
Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)
<h1><strong>Overview</strong></h1> <p>The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023. </p> <h1><strong>Maps Accuracy</strong></h1> <p>The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1.</p> <p>Table 1: Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class.</p> <table> <tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2019</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2020</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2021</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2022</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2023</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> </tr> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> </tr> <tr> <td> <p><span>Forest</span></p> </td> <td> <p><span>0.87</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>1</span></p> </td> </tr> <tr> <td> <p><span>Non Forest</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>1</span></p> </td> <td> <p><span>0.87</span></p> </td> </tr> <tr> <td> <p><strong><span>Overall Accuracy</span></strong></p> </td> <td> <p><strong><span>0.92</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.90</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.94</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> </tr> </tbody> </table> <h1><strong>Files descripion</strong></h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul> <p> </p>
Dakar land use map at street block level
<p>This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.</li> <li>"Dakar_landuse_style.zip" : Files for style of the shapefile.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_AGRI" : Probability to belong to class AGRI</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_DEPR" : Probability to belong to class DEPR</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes</li> </ul> <p>Legend classes label:</p> <ul> <li>"AGRI" : Agricultural vegetation</li> <li>"VEG" : Natural vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"DEPR" : Deprived residential built-up</li> <li>"UNCERT" : Uncertain classification</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Replication data for: Mapping oak wilt disease from space using land surface phenology
Open the record for dataset details and reuse information.
Future land use maps for the Netherlands for the Dutch One Health SSPs
Open the record for dataset details and reuse information.
Regional Land Use mapping code and dataset for Early and Middle Neolithic Ireland (3750-3600 cal BC and 3600-3400 cal BC, respectively)
<p>Dataset and mapping code associated with an example of regional land use mapping for prehistoric Ireland, produced for Fig 5 of paper by S. Harrison et al. '<em>Development and testing of scenarios for implementing Holocene LULC in Earth System Model Experiments'</em> submitted for publication to Geoscientific Model Development. Data points derive from <sup>14</sup>C dated archaeological sites and distributions of settlements and monuments that have been assigned to each archaeological period following the dataset published in McLaughlin et al. (2016).</p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p>
POI-based land use map for Africa
<p>The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. We developed a scalable POI-based land use modeling framework. By combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. </p>
Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p>
Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data
<p>This dataset contains a land cover map of Poitiers in 1993 over an area of 225km².</p> <p>The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution.</p> <p>Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset.</p> <p>As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users.</p> <p>This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.</p>
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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.