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5,023 results for “landing”
A High-Density Land cover Validation dataset in Xinjiang—HDLV-XJ
<p>A 2020 High-Density Land cover Validation dataset in Xinjiang. To ensure a sufficient number of samples in complex areas and to position appropriate sampling points in homogeneous and heterogeneous areas, the equal-area stratified random sampling method based on multiple indicators was utilized. </p><p>The HDLV_XJ includes 20,932 validation samples. It considerably higher sample numbers for each land cover type compared to the other datasets.The HDLV-XJ provides representative validation data with sufficient samples for rare categories, enabling a more accurate assessment of the accuracy of land cover products in the Xinjiang. We provide an xls file of this validation dataset and the code used in constructing the dataset.</p><p> </p>
Sühnekreuz Obercrinitz (Zwickauer Land)
http://www.suehnekreuz.de/ Source: Objaverse 1.0 / Sketchfab
Sühnekreuz Wolfersgrün (Zwickauer Land)
http://www.suehnekreuz.de/ Source: Objaverse 1.0 / Sketchfab
Game Boy GBC - Wario Land 2 - Wario
Source: Objaverse 1.0 / Sketchfab
Game Boy GBC - Wario Land 2 - Wario - Side View
Source: Objaverse 1.0 / Sketchfab
Game Boy GBC - Wario Land 2 - Wario - Back View
Source: Objaverse 1.0 / Sketchfab
Military Land Rover
Source: Objaverse 1.0 / Sketchfab
All-weather 1km land surface temperature at global scale from 2000-2020 from MODIS data
<p>All-weatherLand Surface Temperature product (2000-2020): LSTs from Moderate Resolution Imaging Spectroradiometer(MODIS)/Terra have been produced. The LST data were generated by integrating multiple data from MODIS, reanalysis, and ground in situ measurements using meachine learning method. </p> <ul> <li>The dataset is organized by year.</li> <li>The data is stored in tif format.</li> </ul>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
ELITE land surface temperature: FY-4A/AGRI hourly 4km seamless LST (2023.1-2023.5)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth's radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 4 km LST dataset covering the FY-4A/AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W). First, an improved temperature and emissivity separation algorithm was used to obtain the clear-sky LST. Then, under the framework of the SEB theory, a unique way was proposed to solve the temperature difference between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects. The in situ validation results show that the bias (RMSE) of the AGRI hourly seamless LST is 0.02 K (2.84 K). The temporal resolution and spatial resolution of this dataset are 1 hour and 4 km, respectively.</p> <p>This is the ELITE FY-4A/AGRI seamless LST product in 2023. Please <a href="../records/10595576"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2023.1-2023.5</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Liu, W., Cheng, J. & Wang, Q. (2023). Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory. <em>IEEE Transactions on Geoscience and Remote Sensing, 61</em>,<em> 5001518</em></li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2022.7-2022.12)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn/">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2022.7-2022.12. Please <a href="../records/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2022.7-2022.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Land Subsidence PSI Measurements (2021-2023) in Oran, Algeria, Using Copernicus Sentinel-1 Data and SNAPPING Service
<p>We have processed data from the Copernicus Sentinel-1 satellite, covering the period from January 2021 to September 2023, over the borader area (around 48400 square kilometers) of Oran, Algeria. This analysis utilized the SNAPPING Persistent Scatterers Interferometry (PSI) service from the Geohazards Exploitation Platform (GEP; accessible at <a href="https://geohazards-tep.eu">https://geohazards-tep.eu</a>). Processing was performed by the EO.Lab of AUTh. </p> <p>Measurements contain average Line-of-Sight (LoS) velocities, corresponding uncertainties, and the complete displacement time series. The dataset contains ~3M points covering approximately 7800 sq.km (density of 385 points/km). </p> <p>References</p> <p>[1] Foumelis, M.; Delgado Blasco, J.M.; Brito, F.; Pacini, F.; Papageorgiou, E.; Pishehvar, P.; Bally, P. SNAPPING Services on the Geohazards Exploitation Platform for Copernicus Sentinel-1 Surface Motion Mapping. Remote Sens. 2022, 14, 6075. <a href="https://doi.org/10.3390/rs14236075">https://doi.org/10.3390/rs14236075</a></p> <p>[2] SNAPPING – Surface motioN mAPPING Sentinel-1 on-demand processing service, Online tutorial, <a href="https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html">https://docs.terradue.com/geohazards-tep/tutorials/Snapping.html</a>.</p>
Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment
<p>This is a dataset used in the paper entitled "Integrating the interconnections between groundwater and land surface processes through the coupled NASA Land Information System and ParFlow environment" by Maina et al., 2024</p>
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>
Roles of Forests in Moderating the Diurnal Cycle of Land-Atmosphere Interactions
Open the record for dataset details and reuse information.
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.</p>
Rates of Vertical Land Motion (VLM) on Tutuila (American Samoa) and Upolu (Samoa) (from 3 December 2015 to 16 April 2020 and from 28 April 2020 to 27 November 2023)
<p>Dataset associated with the resubmitted manuscript under revision "Spatiotemporal Patterns of Subsidence and Sea Level Rise in the Samoan Islands Nearly 15 Years after the 2009 Samoa-Tonga Earthquake" by S. A. Huang, J. M. Sauber, S.-C. Han, R. Ray, and E. J. Fielding.</p> <p>Average VLM rates are provided for Tutuila (American Samoa) and Upolu (Samoa) over two subsets:</p> <ol> <li>3 December 2015 to 16 April 2020</li> <li>28 April 2020 to 27 November 2023</li> </ol> <p>VLM rates were derived using the PS-InSAR technique described in the publication at https://doi.org/10.1109/LGRS.2024.3358737. </p> <p>The following files are included here:</p> <ul> <li>plot_ps_data_tutuila_upolu.m: Matlab script to visualize the data included in all the *.mat files (Figures 4 and 5 and the histograms in 8c and 9c without annotations). Requires Computer Vision Toolbox.</li> </ul> <p>Upolu:</p> <ul> <li>upolu_ampl.mat: Amplitude backscatter from Upolu averaged from all scenes, multilooked for speckle reduction and resampled to the dataset size. Normalized to 1.</li> <li>latvals.mat: Latitude vector associated with both Upolu datasets.</li> <li>lonvals.mat: Longitude vector associated with both Upolu datasets.</li> <li>upolu_15-20_vlm.mat: Computed VLM rates across Upolu for the 2015-2020 time period [mm/yr]. All non-PS have an "NaN" value.</li> <li>upolu_15-20_vlm_uncert.mat: Uncertainty of VLM across Upolu for the 2015-2020 time period [mm/yr]. All non-PS have an "NaN" value.</li> <li>upolu_20-23_vlm.mat: Computed VLM rates across Upolu for the 2020-2023 time period [mm/yr]. All non-PS have an "NaN" value.</li> <li>upolu_20-23_vlm_uncert.mat: Uncertainty of VLM across Upolu for the 2020-2023 time period [mm/yr]. All non-PS have an "NaN" value.</li> </ul> <p>Tutuila: </p> <ul> <li>tutuila_ampl.mat: Amplitude backscatter from Tutuila averaged from all scenes, multilooked for speckle reduction and resampled to the dataset size. Normalized to 1.</li> <li>tutuila_latvals.mat: Latitude vector associated with both Tutuila datasets.</li> <li>tutuila_lonvals.mat: Longitude vector associated with both Tutuila datasets.</li> <li>tutuila_15-20_vlm.mat: Computed VLM rates across Tutuila Island for the 2015-2020 time period [mm/yr]. All non-PS have an "NaN" value.</li> <li>tutuila_15-20_vlm_uncert.mat: Uncertainty of VLM across Tutuila Island for the 2015-2020 time period[mm/yr]. All non-PS have an "NaN" value.</li> <li>tutuila_20-23_vlm.mat: Computed VLM rates across Tutuila Island for the 2020-2023 time period [mm/yr]. All non-PS have an "NaN" value.</li> <li>tutuila_20-23_vlm_uncert.mat: Uncertainty of VLM across Tutuila Island for the 2020-2023 time period[mm/yr]. All non-PS have an "NaN" value.</li> </ul>
ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method
<p><span><span>We develope <span><span> </span>a consistent annual land cover product for China (ALCC) from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11±0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>
Supporting Data for "Innovative reforestation mosaics on marginal land in the globally important Mata Atlântica biome can create climate and economic co-benefits"
<p>This data is related to the article "Innovative reforestation mosaics on marginal land in the globally important Mata Atlântica biome can create climate and economic co-benefits."</p>
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.