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240 results for “High resolution maps”
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
<p><strong>Project Page</strong></p> <p><a href="https://open-earth-map.org/">https://open-earth-map.org/</a></p> <p><strong>Paper</strong></p> <p><a href="https://arxiv.org/abs/2210.10732">https://arxiv.org/abs/2210.10732</a></p> <p><strong>Overview</strong></p> <p>OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.</p> <p><strong>Reference</strong></p> <pre><code>@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }</code></pre> <p><strong>License</strong></p> <p>Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data <a href="https://open-earth-map.org/attribution.html">here</a>. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0</a> International License.</p> <p><strong>Note for xBD data</strong></p> <p>The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from <a href="https://xview2.org/dataset">https://xview2.org/dataset</a> and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.</p> <p><strong>Code</strong></p> <p>Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available <a href="https://github.com/bao18/open_earth_map">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>Performance on the test set can be evaluated on the <a href="https://codalab.lisn.upsaclay.fr/competitions/9121">Codalab webpage</a>.</p>
Using the Hybrid U-Net model based on 2 m multisource high resolution for mapping mangroves
<p>Mangrove wetlands, U-Net, multisource high-resolution, remote sensing, mangrove habit</p>
Prospective Evaluation of Open Irrigated Ablation Catheters With High Resolution Mapping to Treat Paroxysmal Atrial Fibrillation
ClinicalTrials.gov study NCT03729830. IPD Sharing: NO. Countries: 6. Publications: 0.
High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data
Open the record for dataset details and reuse information.
Data from: Mapping migration in a songbird using high-resolution genetic markers
Open the record for dataset details and reuse information.
Vegetation and Open Water High-Resolution Maps for Selected US Tidal Marshes, 2015
This dataset provides maps of tidal marsh green vegetation, non-vegetation, and open water for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from current National Agriculture Imagery Program data (2013-2015) using object-based classification for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program (C-CAP) map. These 1m resolution maps were used to calculate the fraction of green vegetation within 30m Landsat pixels for the same tidal marsh regions and these data are provided in a related dataset.
Green Vegetation Fraction High-Resolution Maps for Selected US Tidal Marshes, 2015
This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water. Using this high-resolution map, the percent of each class within Landsat pixel extents was calculated to produce a 30m fraction of green vegetation map for each region.
Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015
This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.
High-Resolution Vegetation Community Maps, Toolik Lake Area, Alaska, 2013-2015
This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps.
High-Resolution Shrub Biomass and Uncertainty Maps, Toolik Lake Area, Alaska, 2013
This dataset contains estimates for aboveground shrub biomass and uncertainty at high spatial resolution (0.80-m) across three research areas near Toolik Lake, Alaska. The estimates for August of 2013 were generated and mapped using Random Forest modeling with input variables of optimized LiDAR-derived canopy volume and height, mean NDVI from 4-band RGB color and near-IR orthophotographs, and harvested biomass data. Uncertainty in the final shrub biomass maps was quantified by producing separate maps showing the coefficient of variation (CV) of the Random Forest map estimates. Shrub biomass was harvested at Toolik Lake in 2014 and used to optimize inputs and validate the final model and these biomass data are also provided.
KSHV chromatin looping facilitates effective gene expression: high-resolution mapping of K-Rta binding sites in the KSHV genome
GEO Series GSE99950. Homo sapiens. 6 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis
GEO Series GSE243280. Homo sapiens. 7 samples. Type: Other; Expression profiling by high throughput sequencing.
High resolution nulceotide level methylation map of female mouse primary keratinocytes
GEO Series GSE44918. Mus musculus. 1 samples. Type: Methylation profiling by high throughput sequencing.
High Resolution N6-Methyladenosine (m6A) Map Using Photo-crosslinking-Assisted m6A Sequencing
GEO Series GSE54921. Homo sapiens. 4 samples. Type: Other.
RiboMeth-seq: High-throughput mapping of ribose methylations in RNA at single-nucleotide resolution
GEO Series GSE54655. Saccharomyces cerevisiae. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
A high resolution map of a cyanobacterial transcriptome
GEO Series GSE29264. Synechococcus elongatus PCC 7942 = FACHB-805. 13 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
DNA Methylation-Based High-Resolution Mapping of Long-Distance Chromosomal Interactions in Nucleosome-Depleted Regions (HiC)
GEO Series GSE242396. Saccharomyces cerevisiae. 6 samples. Type: Other.
High-resolution mapping of HP1 redistribution in a position-effect variegation model
GEO Series GSE12395. Drosophila melanogaster. 12 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by genome tiling array.
High resolution 3D chromatin maps of human primary monocytes [Hi-C]
GEO Series GSE157971. Homo sapiens. 12 samples. Type: Other.
High-resolution, genome-wide mapping of positive supercoiling in chromosomes [RNA-Seq 2]
GEO Series GSE164186. Saccharomyces cerevisiae. 7 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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.