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Using the NASA EOSDIS Common Metadata Repository

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  • A Fusion Dataset for Crop Type Classification in Germany

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.xml
    Description:

    This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 52.4179888 13.6339485 52.8494418 14.3529903

    MLHUB Short Name: A Fusion Dataset for Crop Type Classification in Germany Version ID: 1 Unique ID: C2781412484-MLHUB

  • A Fusion Dataset for Crop Type Classification in Western Cape, South Africa

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412697-MLHUB.xml
    Description:

    This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -34.413256 20.5212157 -33.9796334 21.043415

    MLHUB Short Name: A Fusion Dataset for Crop Type Classification in Western Cape, South Africa Version ID: 1 Unique ID: C2781412697-MLHUB

  • ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021

    https://cmr.earthdata.nasa.gov/search/concepts/C2612824429-ORNL_CLOUD.xml
    Description:

    This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 60.76 -164.4 67.21 -143.84

    ORNL_CLOUD Short Name: Alaska_Lake_Pond_Maps_2134 Version ID: 1 Unique ID: C2612824429-ORNL_CLOUD

  • East Africa Agricultural Field Centers

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412176-MLHUB.xml
    Description:

    Georeferenced crop yield prediction is a valuable tool for agronomists and policymakers. One challenge with many existing datasets is that of location accuracy. GPS locations for fields can end up offset from the true location due to sensor inaccuracies or from locations being collected at the edges of fields rather than the field centers. This makes it harder to connect remote-sensed data to the yield values. The goal of this project was to produce a method that can help correct these location offsets by finding the most probable field center given an input location.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -9.2148091 28.9018327 1.229293 37.2322299

    MLHUB Short Name: East Africa Agricultural Field Centers Version ID: 1 Unique ID: C2781412176-MLHUB

  • High Mountain Asia Multitemporal Landslide Inventory for the Pumqu/Arun River Basin V001

    https://cmr.earthdata.nasa.gov/search/concepts/C2217578876-NSIDC_ECS.xml
    Description:

    The transboundary Pumpqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumpqu River flows. The river is named the Arun River once it enters Nepal. Five large hydropower projects (in total about 3,163 MW) are currently under construction or are planned for the Arun River valley. Rainfall and earthquake-induced landslides, landslide dammed lakes, and landslide-induced glacial lake outburst floods pose major risks to the smooth operation of these projects. This data set is a multitemporal landslide inventory covering the whole Pumpqu/Arun River basin. It was generated in support of the World Bank’s Risk Assessment of Landslides in the Upper Arun Hydropower Project.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 26.94 86.93 28.11 87.58

    NSIDC_ECS Short Name: HMA2_MTLI Version ID: 1 Unique ID: C2217578876-NSIDC_ECS

  • Marine Debris Dataset for Object Detection in Planetscope Imagery

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412735-MLHUB.xml
    Description:

    Floating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris on satellite imagery and pave the way to a global monitoring system for controlling and preventing the accumulation of marine debris in oceans. This dataset consists of images of marine debris which are 256 by 256 pixels in size and labels which are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery which has a spatial resolution of approximately 3 meters. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items. Several studies were used for data collection and validation. While a small percentage of the dataset represents the coastlines of Ghana and Greece, most of the observations surround the Bay Islands in Honduras. The marine debris detection models created and the relevant code for using this dataset can be found [here](https://github.com/NASA-IMPACT/marine_debris_ML).

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: 5.4683637 -88.2971191 39.1087514 34.5300293

    MLHUB Short Name: Marine Debris Dataset for Object Detection in Planetscope Imagery Version ID: 1 Unique ID: C2781412735-MLHUB

  • PlanetScope ESA archive

    https://cmr.earthdata.nasa.gov/search/concepts/C2547572362-ESA.xml
    Description:

    The PlanetScope ESA archive collection consists of PlanetScope products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new PlanetScope products. Three product lines for PlanetScope imagery are offered, for all of them the Ground Sampling Distance at nadir is 3.7 m (at reference altitude 475 km) The Basic Scene product is a single-frame scaled Top of Atmosphere Radiance (at sensor) and sensor-corrected product. The product is not orthorectified or corrected for terrain distortions, radiometric and sensor corrections are applied to the data The Ortho Scenes is a single-frame scaled Top of Atmosphere Radiance (at sensor) or Surface Reflectance image product. The product is radiometrically, sensor and geometrically corrected and is projected to a cartographic map (UTM/WGS84) The Ortho Tiles are multiple orthorectified scenes in a single strip that have been merged and then divided according to a defined grid. Radiometric and sensor corrections are applied, the imagery is orthorectified and projected to a UTM projection.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -84 -180 84 180

    ESA Short Name: PlanetScopeESAarchive Version ID: NA Unique ID: C2547572362-ESA

  • PlanetScope Full Archive

    https://cmr.earthdata.nasa.gov/search/concepts/C1965336933-ESA.xml
    Description:

    The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components: - Image File (GeoTIFF format) - Metadata File (XML format) - Rational Polynomial Coefficients (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance: - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components: - Image File (GeoTIFF format) - Metadata File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 3-band natural color (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance: - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and color-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/ can be accessed (Planet registration requested) to discover and check the data readiness. It is worth to mention that the data distribution is based on Data Voucher, corresponding to maximum amount of square kilometers can be ordered and downloaded by the project in a maximum period of 15 moths (this duration cannot be extended) starting from the project proposal acceptance date. Each Date Voucher includes PlanetScope tile view streaming access for a total of 20,000 tiles per calendar month during the project period. All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf available in Resources section.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -90 -180 90 180

    ESA Short Name: PlanetScope.Full.Archive Version ID: NA Unique ID: C1965336933-ESA

  • PlanetScope Satellite Imagery 3 Band Scene

    https://cmr.earthdata.nasa.gov/search/concepts/C2112982481-CSDA.xml
    Description:

    The Planet Scope 3 band collection contains satellite imagery obtained from Planet Labs, Inc by the Commercial Smallsat Data Acquisition (CSDA) Program. This satellite imagery is in the visible waveband range with data in the red, green, and blue wavelengths. These data are collected by Planets Dove, Super Dove, and Blue Super Dove instruments collected from across the global land surface from June 2014 to present. Data have a spatial resolution of 3.7 meters at nadir and provided in GeoTIFF format. Data access are restricted to US Government funded investigators approved by the CSDA Program.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -90 -180 90 180

    CSDA Short Name: PSScene3Band Version ID: 1 Unique ID: C2112982481-CSDA

  • Rwanda Field Boundary Competition Dataset

    https://cmr.earthdata.nasa.gov/search/concepts/C2781412768-MLHUB.xml
    Description:

    This dataset contains field boundaries for smallholder farms in eastern Rwanda. The Nasa Harvest program funded a team of annotators from TaQadam to label Planet imagery for the 2021 growing season for the purpose of conducting the Rwanda Field boundary detection Challenge. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet's NICFI program. Planet's basemap imagery is provided for six months (March, April, August, October, November and December). The paired dataset is provided in 256x256 chips for a total of 70 tiles covering 1532 individual fields.<br><br>Input imagery consists of a time series of planet Basemaps from the NICFI program (monthly composite) data.<br><br>Imagery Copyright 2021 Planet Labs Inc. All use subject to the Participant License Agreement.

    Links: Temporal Extent: Spatial Extent:
    Minimum Bounding Rectangle: -1.5902813 30.2918386 -1.3915649 30.4235458

    MLHUB Short Name: Rwanda Field Boundary Competition Dataset Version ID: 1 Unique ID: C2781412768-MLHUB