Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature v3" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2019, and the bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was removed from each satellite pixel. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2019) to each satellite was removed from all scenes from the corresponding satellite. The errors were determined by using a K-fold cross-validation to minimize error at each satellite pixel. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=Rhode Island Data Discovery/United States Geological Survey publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the C130 flight track in real-time during the ICE-L project. Both LRT and HRT files are included in this dataset.

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

Translation from x,y coordinates to latitude and longitude for the "Landsat Satellite Surface Temperature version 2, Narragansett Bay" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2022, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to or subtrcted from all scenes by satellite. The standard deviation between the buoys and the satellite after adding the bias is 1.9 degrees C for Landsat 5, 1.9 degrees C for Landsat 7 and 1.3 degrees C for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Longitude[0:last][0:last][last]&.draw=surface&.vars=X|Y|Longitude history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2022) to each satellite was added to or subtracted from all scenes from the corresponding satellite. For Landsat 5, 0.45 degrees C was subtracted from all scenes; similarly 1.094 was subtracted for Landsat 7, and 0.178 was added for Landsat 8. The errors were determined by averaging the five closest temporal buoy readings for each satellite image capture and spatially averaging buoy locations within a 200-square-meter zone. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Narragansett Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ institution=Rhode Island Data Discovery/United States Geological Survey publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR HIAPER GV flight track in real-time during the MAIR-E project.

Cette présentation examine Google Earth, l'utilisation de données GPS, la cartographie KML des données du recensement, ainsi que le géocodage et géoréférencement d’images.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

KMZ file with Kongsberg EM302 multibeam survey results from the NOAA Ship Okeanos Explorer during EX1202L3

Landsat-derived water surface temperature in Mount Hope Bay with bias correction from RI DEM buoys. For translation from the x,y coordinates to latitude and longitude see the "Landsat Satellite Coordinates version 2, Mt. Hope Bay" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2022, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to or subtrcted from all scenes by satellite. The standard deviation between the buoys and the satellite after adding the bias is 1.9 degrees C for Landsat 5, 1.9 degrees C for Landsat 7 and 1.3 degrees C for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.10, ACDD-1.3 defaultGraphQuery=temperature[0:last][0:last][last]&.draw=surface&.vars=X|Y|temperature description=Landsat-derived water surface temperature in Mount Hope Bay with bias correction from RI DEM buoys history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Mount Hope Bay (2003-2022) to each satellite was added to or subtracted from all scenes from the corresponding satellite. For Landsat 5, 0.45 degrees C was subtracted from all scenes; similarly, 1.094 was subtracted for Landsat 7, and 0.178 was added for Landsat 8. The errors were determined by averaging the five closest temporal buoy readings for each satellite image capture and spatially averaging buoy locations within a 200-square-meter zone. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. A buoy comparison was only conducted within Mount Hope Bay for Landsat scenes with less than 50% cloud cover, and applied to available scenes back to 1984 for Landsat 5, 1999 for Landsat 7, and 2013 for Landsat 8. As a result, data uncertainties are unknown outside of the Mount Hope Bay region, for scenes with greater cloud cover, and scenes before 2003. infoUrl=https://earthexplorer.usgs.gov institution=Rhode Island Data Discovery/United States Geological Survey keywords_vocabulary=GCMD Science Keywords publication=https://doi.org/10.26300/ja0b-xa86 references=Rhode Island Data Discovery/United States Geological Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 time_coverage_end=2022-09-29T00:00:00Z time_coverage_start=1984-05-02T00:00:00Z

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR C-130 flight track in real-time during the ARISTO2015 project.

This data package includes 481 geospatial vector polygons of historic fire perimeters associated with National Wildlife Refuges in Alaska. These polygons, originally held within the Alaska Large Fire Database (ALFD), were reviewed and found to have geospatial inaccuracies with respect to the fire they represent. They were corrected and updated based on a variety of remote sensing resources; the Monitoring Trends in Burn Severity (MTBS) database (https://www.mtbs.gov/viewer/index.html), geospatial rasters of historic fire activity derived from Landsat 1-9 imagery in the Google Earth Engine(https://earthengine.google.com) environment, and historical air photos acquired through EarthExplorer (https://earthexplorer.usgs.gov/). Wildfire records occurring within Alaska Wildlife Refuge units and considered for necessary updates, spanned the 1943-2022 time period and comprised 1,229 recorded fires. After reviewing all fire records, 400 fire perimeters were updated and 81 previously unrecorded fires were added to the database. These updated records spanned 1954-2021.

Landsat-derived water surface temperature near Narragansett Bay with bias correction from RI DEM buoys (lakes and rivers included). For translation from the x,y coordinates to latitude and longitude see the "Landsat Satellite Coordinates version 1" dataset. cdm_data_type=Grid comment=Attribute Accuracy Report: Satellite-derived orthorectified brightness temperature was measured within 0.1 degrees C for Landsat 8, 0.6 degrees C for Landsat 7, and 0.5 degrees C for Landsat 5. Satellite measurements were compared to in situ (buoy) surface temperatures from 2003 to 2015, and the mean bias between the RI DEM buoy temperatures and the satellite temperatures at the pixel of the buoys was added to all scenes by satellite. The standard deviation between the buoys and the satellie after adding the bias is 1.9 for Landsat 5, 1.9 for Landsat 7 and 1.3 for Landsat 8. The standard deviation is considered the uncertainty of the satellite measurements. See https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance?qt-science_support_page_related_con=0#qt-science_support_page_related_con for more information. Conventions=COARDS, CF-1.6, ACDD-1.3 defaultGraphQuery=Temp[0:last][0:last][last]&.draw=surface&.vars=X|Y|Temp history=Converted from Landsat 5, 7, and 8 Surface Reflectance geotiff products to netCDF. The units were changed from K to degrees C and the average bias determined through RI DEM buoy comparison in Narragansett Bay (2003-2015) to each satellite was added to all scenes from the corresponding satellite. For Landsat 5, 3.36 degrees C was added to all scenes, 3.34 for Landsat 7, and 1.92 for Landsat 8. The errors were determined by calculating the standard deviation of the difference between Landsat and buoy temperature at buoy locations for each satellite. The scenes were also cloud masked, land masked, and stripes in Landsat 7 imagery (due to sensor failure) were masked as well. Data outside of the Narragansett Bay region and for all cloud cover is included, though a buoy comparison was only conducted within Narragansett Bay for Landsat scenes with less than 50% cloud cover. As a result, data uncertainties are unknown outside of the Narragansett Bay region and for scenes with greater cloud cover. infoUrl=https://earthexplorer.usgs.gov institution=United States Geological Survey keywords_vocabulary=GCMD Science Keywords references=United States Geolocical Survey source=https://earthexplorer.usgs.gov/ accessed through https://code.earthengine.google.com/ sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 testOutOfDate=now-71days time_coverage_end=2021-04-22T15:26:44Z time_coverage_start=1984-05-02T14:54:17Z

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the GV flight track in real-time during thePACDEX project.

Geoscience Australia has been updating its collection of navigation for marine seismic surveys in Australia. These include original navigation files, the 2003 SNIP navigation files and digitised survey track maps. The result will be an updated cleansed navigation collection.

The collection is based on the SNIP format P190 navigation file which follows the UKOOA standard. Industry standard metadata associated with a seismic survey is preserved.

To assist industry, Geoscience Australia is making available its updated version of cleansed navigation. Although the process of updating the navigation data is ongoing and there is still legacy data to check, the navigation data is at a point where a significant improvement has been achieved and it is now usable. Users should be aware that this navigation is not final and there may be errors. Geoscience Australia (email - AusGeodata@ga.gov.au) appreciates being notified of any errors found.

The data is available in both KML and Shape file formats.

The KML file can be viewed using a range of applications including Google Earth, NASA WorldWind, ESRI ArcGIS Explorer, Adobe PhotoShop, AutoCAD3D or any other earth browser (geobrowser) that accepts KML formatted data.

Alternatively the Shape files can be downloaded and viewed using any application that supports shape files.

Disclaimer: Geoscience Australia gives no warranty regarding the data downloads provided herein nor the data's accuracy, completeness, currency or suitability for any particular purpose. Geoscience Australia disclaims all other liability for all loss, damages, expense and costs incurred by any person as a result of relying on the information in the data downloads.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR HIAPER G-V flight track in real-time during the NOREASTER project.

For the needs of the “Portal for heritage buildings integration into the contemporary built environment”, in short, PERIsCOPE project, satellite observations were applied. This repository includes the results from the macro-scale analysis, for which thermal data, optical satellite images, and ready satellite products were exploited to provide multi-temporal information.

Satellite-based products estimate the temperature variations in a broader area for the selected urban testbeds (Limassol and Strovolos municipalities, Cyprus). Landsat 7 and 8 archives were downloaded through the EarthExplorer platform (“Landsat Collection 1 Level-1” for both “Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1” and “Landsat 8 OLI/TIRS C1 Level-1”). The Level-1 data downloaded from the platform were rescaled to the top of atmosphere (TOA) reflectance and radiance using radiometric rescaling coefficients provided in the metadata file delivered with the Level-1 product (metadata—MTL file).

More than 140 satellite images were selected (a cloud coverage filter was applied), downloaded, and processed, covering the period between 2013 and 2020. Specifically, 16 images during the Winter season, 30 images over Spring, 57 images for Summer, and 38 during Autumn were finally gathered for both case studies.

The Google Earth Engine cloud platform infrastructure was used to extract optical products, namely the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-up Index (NDBI), which characterise vegetated and built-up areas, respectively.

This repository concerns: (1) the mean Land Surface Temperature (LST) for both test sites (from 2013-2020), their standard deviation, and the maximum and minimum values. In addition, the (2) NDVI and (3) NDBI products per year (2013-2020) are provided. Lastly, (4) the seasonal variations are included.

The data are structured in both forms of ArcGIS Pro Geodatabase (.gdb), while individual .tiff formats are provided in each folder.

Further details can be found in the following references:

1. Agapiou, A.; Lysandrou, V. Observing Thermal Conditions of Historic Buildings through Earth Observation Data and Big Data Engine. Sensors 2021, 21, 4557. https://doi.org/10.3390/s21134557

2. Agapiou A., Lysandrou V., Cuca B., Copernicus earth observations for cultural heritage, Proceedings of the joint international event, 9th ARQUEOLÓGICA 2.0 & 3rd GEORES, Valencia (Spain). 26–28 April 2021, DOI: https://doi.org/10.4995/Arqueologica9.2021.12512

Geoscience Australia is releasing its 2014 version of the Marine Seismic Surveys Shape and Kml files. These files have been updated to include recent openfile surveys. The spatial files have been created from a cleansed, updated collection of p190 navigation files. This navigation collection has grown from the checking of navigation submitted to the GA Repository under the Offshore Petroleum and Greenhouse Gas Storage Regulations, checking of the 2003 SNIP navigation files and the digitisation of old survey track maps as required. Soon the individual p190 files will be available for download through the new NOPIMS delivery system. The collection is based on P190 navigation files which follows the UKOOA standard. Extensive industry standard metadata associated with a seismic survey is preserved in the attribute tables of these datasets.

The shapefiles have been categorised into 3D exploration, 2D exploration and 2D investigative seismic files. All marine surveys undertaken by Geoscience Australia for exploration or investigative purposes have been included in the collection. Geoscience Australia (email - AusGeodata@ga.gov.au) appreciates being notified of any errors found in the navigation collection.

The data is available in both KML and Shape file formats.

The KML file can be viewed using a range of applications including Google Earth, NASA WorldWind, ESRI ArcGIS Explorer, Adobe PhotoShop, AutoCAD3D or any other earth browser (geobrowser) that accepts KML formatted data.

Alternatively the Shape files can be downloaded and viewed using any application that supports shape files.

Disclaimer: Geoscience Australia gives no warranty regarding the data downloads provided herein nor the data's accuracy, completeness, currency or suitability for any particular purpose. Geoscience Australia disclaims all other liability for all loss, damages, expense and costs incurred by any person as a result of relying on the information in the data downloads.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

KML is a file format used to display geographic data in an Earth browser such as Google Earth. This dataset contains KML files used to display the NSF/NCAR HIAPER GV flight track in real-time during the ECLIPSE.