This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

This layer presents a nighttime view of the Earth that provides an informational and educational view of our planet at night. The image was produced by mosaicking Defense Meteorlogical Satellite Program (DMSP) Operational Linescan System (OLS) satellite images. This system was originally designed to view clouds by moonlight and to map the locations of permanent lights on the Earth's surface. These data are derived from 9 months of observations superimposed on a darkened land surface. ESRI georeferenced these data to a real-world coordinate system.The layer is suitable for display to a largest scale of 1:18,500,000, though this map over-samples it down to ~1:1,4,500,000 scale for context purposes. The layer does not display at all below that scale.The web map also includes a Boundaries and Places layer that can be turned on to see country boundaries and major cities.To access the source imagery used to publish this map service, you can use the Earth at Night Layer Package that is published by Esri.

This dataset contains normal maps of the surface of planets, moons and dwarf planets of the solar system encoded as RGB images. These maps can be used to generate synthetic images in physical-based render engines, such as ABRAM or Blender.

The maps were obtained processing available open-source displacement maps of the corresponding bodies with algorithms based on plane fitting or mean of the normals of the neighbouring triangles.

They are provided in Normal Body Map (NBM) format or Normal Local Map (NLM) format depending on the frame of reference the vectors are defined (body-fixed or south-east-upward local).

Higher-resolution of these maps are available upon request to the author.

Understanding natural and human systems is an essential first step toward reducing the severity of climate change and adapting to a warmer future. Maps and geographic information systems are the primary tools by which scientists, policymakers, planners, and activists visualize and understand our rapidly changing world. Spatial information informs decisions about how to build a better future. This Story Map Journal was created by Esri's story maps team. For more information on story maps, visit storymaps.arcgis.com.

A collection of 3 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Incentive-boosted inhibitory control task (Planets task). Participants (n=76) completed 2 runs of an incentive-boosted inhibitory control task called the Planets task while undergoing an fMRI scan. The Planets task is a Go/No-Go cognitive control task in a monetary reward context. The task took approximately 13.5 minutes to complete. In this task, adolescent participants are told to press a button with their dominant index finger whenever they see a crater planet (i.e., Go trials), but to withhold their response (i.e., not press the button) whenever they see a striped planet (i.e., No-Go trials). If participants successfully press a button after seeing a crater planet or avoid pressing a button after seeing a stripped planet, then they earn a monetary reward. In contrast, if participants do not press a button after seeing a crater planet or press a button after seeing a striped planet, then they lose a monetary reward.
How much adolescents can earn or lose on each trial depends on the incentive context (Boost-Type) of the block (i.e., set of Go/No-Go trials), indicated by the cue presented prior to the start of the block. If the block is cued with a pink fractal, then adolescents can earn $1 and lose $0.5 on each trial (i.e., Big-Boost: high monetary stakes). If the block is cued with a blue fractal, then adolescents can earn $0.2 and lose $0.1 on each trial (i.e., Small-Boost: low monetary stakes). Finally, if the block is cued with a yellow fractal, then adolescents cannot win or lose money and it is a baseline cognitive control performance block (No-Boost: no monetary stakes). Adolescents learned information about planets and fractals during the practice session and were reminded before each run.

The goal of this project is to create a map of the planet Mars, by using ESRI software. For this, a 3D project was developed using ArcGIS Pro, considering a global scene, to be published in an online platform. All the various data from Mars will be available in a single website, where everyone can visualize and interact. The Red Planet has been studied for many decades and this year marks the launch of a new rover, Mars2020, which will happen on the 17th of July. This new rover will be continuing the on-going work of the Curiosity Rover, launched in 2012. The main objective for these rovers is to determine if Mars could have supported life, by studying its water, climate and geology. Currently, the only operational rover in Mars is Curiosity and with that in mind, this project will have a strong focus on the path taken by this rover, during almost 8 years of exploration. In the web application, the user will be able to see the course taken by Curiosity in Mars’ Gale Crater, from its landing until January 2020. The map highlights several points of interest, such as the location after each year passed on MarsEarth year and every kilometer, which can be interacted with as well as browse through photos taken at each of the locations, through a pop-up window. Additionally, the application also supports global data of Mars. The two main pieces, used as basemaps, are the global imagery, with a pixel size of 925 meters and the Digital Elevation Model (DEM), with 200 meters per pixel. The DEM represents the topography of Mars and was also used to develop Relief and Slope Maps. Furthermore, the application also includes data regarding the geology of the planet and nomenclature to identify regions, areas of interest and craters of Mars. This project wouldn’t have been possible without NASA’s open-source philosophy, working alongside other entities, such as the European Space Agency, the International Astronomical Union and the Working Group for Planetary System Nomenclature. All the data related to Imagery, DEM raster files, Mars geology and nomenclature was obtained on USGS Astrogeology Science Center database. Finally, the data related to the Curiosity Rover was obtained on the portal of The Planetary Society. Working with global datasets means working with very large files, so selecting the right approach is crucial and there isn’t much margin for experiments. In fact, a wrong step means losing several hours of computing time. All the data that was downloaded came in Mars Coordinate Reference Systems (CRS) and luckily, ESRI handles that format well. This not only allowed the development of accurate analysis of the planet, but also modelling the data around a globe. One limitation, however, is that ESRI only has the celestial body for planet Earth, so this meant that the Mars imagery and elevation was wrapped around Earth. ArcGIS Pro allows CRS transformation on the fly, but rendering times were not efficient, so the workaround was to project all data into WGS84. The slope map and respective reclassification and hillshading was developed in the original CRS. This process was done twice: one globally and another considering the Gale Crater. The results show that the crater’s slope characteristics are quite different from the global panorama of Mars. The crater has a depression that is approximately 5000 meters deep, but at the top it’s possible to identify an elevation of 750 meters, according to the altitude system of Mars. These discrepancies in a relatively small area result in very high slope values. Globally, 88% of the area has slopes less than 2 degrees, while in the Gale Crater this value is only 36%. Slopes between 2 and 10 degrees represent almost 60% of the area of the crater. On the other hand, they only represent 10% of the area globally. A considerable area with more than 10 degrees of slope can also be found within the crater, but globally the value is less than 1%. By combining Curiosity’s track path with the DEM, a profile graph of the path was obtained. It is possible to observe that Curiosity landed in a flat area and has been exploring in a “steady path”. However, in the last few years (since the 12th km), the rover has been more adventurous and is starting to climb the crater. In the last 10 km of its journey, Curiosity “climbed” around 300 meters, whereas in the first 11 km it never went above 100 meters. With the data processed in the WGS84 system, all was ready to start modelling Mars, which was firstly done in ArcGIS Pro. When the data was loaded, symbology and pop-ups configured, the project was exported to ArcGIS Online. Both the imagery and elevation layer were exported as “hosted tile service”. This was a key step, since keeping the same level of detail online and offline would have a steep increase in imagery size, to hundreds of Terabytes, thus a lot of work was put into balancing tile cache size and the intended quality of imagery. For the remaining data, it was a straight-forward step, exporting these files as vectors. Once all the data was in the Online Portal, a Global Web Scene was developed. This is an on-going project with an outlook to develop the global scene into an application with ESRI’s AppBuilder, allowing the addition of more information. In the future, there is also interest to increment the displayed data, like adding the paths taken by other rovers in the past, alongside detailed imagery of other areas beyond the Gale Crater. Finally, with 2021 being the year when the new rover Mars2020 will land on the Red Planet, we might be looking into adding it to this project.https://arcg.is/KuS4r

These are global maps of dwarf planet (1) Ceres described in "Resolved spectrophotometric properties of the Ceres surface from Dawn Framing Camera images" by Schröder et al., Icarus 288 (2017) 201-225 (arXiv). Global color maps created from these data files are shown in Fig. 7 of that paper.

These files contain global maps of the photometrically corrected reflectance of the Ceres surface, with one map for each of the 7 narrow-band filter of the Dawn framing camera (FC2). Photometrically corrected reflectance means observed reflectance divided by model reflectance, and the global average of the photometrically corrected reflectance is unity by definition. The model is the Akimov photometric model with parameters valid for global average Ceres and an upper limit of 80° for the photometric angles. The model reflectance was calculated with surface topography derived from a global shape model. Global maps were created as the median of projected individual images of the photometrically corrected reflectance. The IMG and FITS files contain the data in floating point format. The PNG files are provided for visual reference.

The Unpublished Digital Geologic-GIS Map of Tuzigoot National Monument, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (tuzi_geology.gdb), a 10.1 ArcMap (.MXD) map document (tuzi_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (moca_tuzi_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (moca_tuzi_geology_gis_readme.pdf). Please read the moca_tuzi_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (tuzi_geology_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/tuzi/tuzi_geology_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:48,000 and United States National Map Accuracy Standards features are within (horizontally) 24.4 meters or 80 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 12N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Tuzigoot National Monument.

Natural Earth is a public domain map dataset available at 1:10, 1:50 and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Digital Geomorphic-GIS Map of Cape Lookout National Seashore, North Carolina (1:10,000 scale 2008 mapping) is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (calo_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (calo_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (calo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (calo_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (calo_geomorphology_metadata_faq.pdf). Please read the calo_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: East Carolina University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (calo_geomorphology_metadata.txt or calo_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:10,000 and United States National Map Accuracy Standards features are within (horizontally) 8.5 meters or 27.8 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Bright Earth eAtlas Basemap dataset collection is a satellite-derived global map of the world at a 1:1M scale for most of the world and 1:200k scale for Australia. This map was inspired by Natural Earth II (NEII) and NASA's Blue Marble Next Generation (BMNG) imagery.

Its aim was to provide a basemap similar to NEII but with a higher resolution (~10x).

This basemap is derived from the following datasets: Blue Marble Next Generation 2004-04 (NASA), VMap0 coastline, Coast100k 2004 Australian coastline (GeoScience Australia), SRTM30 Plus v8.0 (UCSD) hillshading, Natural Earth Vector 10m bathymetry and coastline v2.0 (NE), gbr100 hillshading (JCU).

This dataset (World_Bright-Earth-e-Atlas-basemap) contains all the files required to setup the Bright Earth eAtlas basemap in a GeoServer. All the data files are stored in GeoTiffs or shapefiles and so can also be loaded into ArcMap, however no styling has been included for this purpose.

This basemap is small enough (~900 MB) that can be readily used locally or deployed to a GeoServer.

Base map aesthetics (added 28 Jan 2025)

The Bright Earth e-Atlas Basemap is a high-resolution representation of the Earth's surface, designed to depict global geography with clarity, natural aesthetics with bright and soft color tones that enhance data overlays without overwhelming the viewer. The land areas are based on NASA's Blue Marble imagery, with modifications to lighten the tone and apply noise reduction filtering to soften the overall coloring. The original Blue Marble imagery was based on composite satellite imagery resulting in a visually appealing and clean map that highlights natural features while maintaining clarity and readability. Hillshading has been applied across the landmasses to enhance detail and texture, bringing out the relief of mountainous regions, plateaus, and other landforms.

The oceans feature three distinct depth bands to illustrate shallow continental areas, deeper open ocean zones, and the very deep trenches and basins. The colors transition from light blue in shallow areas to darker shades in deeper regions, giving a clear sense of bathymetric variation. Hillshading has also been applied to the oceans to highlight finer structures on the seafloor, such as ridges, trenches, and other geological features, adding depth and dimensionality to the depiction of underwater topography.

At higher zoom levels prominent cities are shown and the large scale roads are shown for Australia.

Rendered Raster Version (added 28 Jan 2025)

A low resolution version of the dataset is available as a raster file (PNG, JPG and GeoTiff) at ~2 km and 4 km resolutions. These rasters are useful for applications where GeoServer is not available to render the data dynamically. While the rasters are large they represent a small fraction of the full detail of the dataset. The rastered version was produced using the layout manager in QGIS to render maps of the whole world, pulling the imagery from the eAtlas GeoServer. This imagery from converted to the various formats using GDAL. More detail is provided in 'Rendered-bright-earth-processing.txt' in the download and browse section.

Change Log 2025-01-28: Added two rendered raster versions of the dataset at 21600x10800 and 10400x5400 pixels in size in PNG, JPG and GeoTiff format. Added

Globally available mosaics at a cell size of 4.77 m (at equator) using Mercator projection. Planet Basemaps are offered at a weekly, biweekly, monthly, or quarterly cadence and provide a clear, comprehensive view of an area of interest by automating the selection of the best individual images to create a complete, seamless, and recent “mosaic” that allows you to understand change over broad areas. Planet Basemaps are also well suited for machine learning-analytics by applying cloud masking and radiometric normalization to allow for consistent data across large geographic areas of interest.

The Barest Earth Sentinel-2 Map Index dataset depicts the 1 to 250 000 maps sheet tile frames that have been used to generate individual tile downloads of the Barest Earth Sentinel-2 product. This web service is designed to be used in conjunction with the Barest Earth Sentinel-2 web service to provide users with direct links for imagery download.

Dataset Overview

Follow up to my previous terrain dataset ( https://www.kaggle.com/tpapp157/terrainimagepairs ) with a number of improvements.

This is a dataset I compiled composed of 5000 image sets. Each set represents a random 512x512 pixel crop of the Earth and is composed of a Terrain map, a Height map, and a Segmentation map. Image sets are numbered 0000 through 5000 with a suffix of '_t' for terrain, '_h' for height, and '_i2' for segmentation. Crops were dynamically adjusted based on latitude to compensate for map projection distortion and maintain a relatively consistent land feature size across the dataset (1px ~ 400m).

Terrain Maps

Terrain Maps are colored based on land type with relief shading. Images are standard uint8 png format.

Height Maps

Height Maps encode altitude information through pixel value (0 being sea level). Note that these images are single channel, uint16.

Segmentation Maps

These were generated based on the terrain and height maps using unsupervised clustering and classification techniques for local pixel regions. In total, 7 terrain categories were defined for segmentation and associated with representative colorings as follows:

• (17, 141, 215): Water
• (225, 227, 155): Grassland
• (127, 173, 123): Forest
• (185, 122, 87): Hills
• (230, 200, 181): Desert
• (150, 150, 150): Mountain
• (193, 190, 175): Tundra

Maps were then median filtered to remove noise and smooth out the features into larger blobs. Each segmentation map was created with some randomized parameters to create more variety across the dataset and ensure that image sets which happened to overlap would not have exactly the same segmentation map.

Code

Reference this github for discussion and code regarding a GAN I trained with this dataset to translate user created segmentation maps into Texture and Height maps for 3D rendering.

https://github.com/tpapp157/SPADE-Terrain-GAN

ADMMR map collection: New Planet Iron Mine Index Map; 1 in. to 500 feet; 40 x 30 in.

Context

I created this dataset to train a network to generate realistic looking terrain maps based on simple color region codings. You can see some samples of the result here: https://www.reddit.com/r/MachineLearning/comments/7dwj1q/p_fun_project_mspaint_to_terrain_map_with_gan/

Content

This is a dataset of 1360 image pairs. The ground truth image is a random 512x512 pixel crop of terrain from a global map of the Earth. The second image is a color quantized and mode filtered version of the base image to create a very simple terrain region mapping composed of five colors. The five colors correspond to terrain types as follows: blue - water, grey - mountains, green - forest/jungle/marshland, yellow - desert/grassland/glacier, brown - hills/badlands.

This dataset represents an update of previous global maps of Martian fluvial systems. We included all the valleys longer than 20 km and mapped them as vector-based polylines within the QGIS software, using the more recent and, to date, the best resolution THEMIS (Thermal Emission Imaging Spectrometer) daytime IR mosaic (100 m/pixel). In addition, we used, where necessary (for small-scale systems and valleys with high erosion), CTX (Contex Camera) data, with a resolution up to 6 m/pixel. The imagery data were coupled with the MOLA (Mars Orbiter Laser Altimeter Mosaic) mosaic which has a spatial resolution of 463 m/pixel. At low latitudes, we used an equidistant cylindrical projection, while at high latitudes, we used sinusoidal and polar stereographic projections to represent and analyze the data. Topographic information and data of higher image quality (new THEMIS mosaic plus CTX data) than those of previous manual maps, allowed us to identify new structures and more tributaries for a large number of systems. An attribute table is associated to our dataset including useful information such as coordinates, total length and an approximative maximum age indication for each system. The latter has been obtained coupling our map with the global geologic map of Tanaka et al. (2014) which represents, to date, the most accurate dating of the planet surface.

Attribution:

If you use this data set in your own work, please cite this DOI:
10.5281/zenodo.1051038

Please also cite these works:

Alemanno et al.: 2018, Global Map of Martian Fluvial Systems: Age and Total Eroded Volume Estimations, Earth and Space Science Journal, 5, 560-577, doi:https://doi.org/10.1029/2018EA000362
Orofino et al.: 2018, Estimate of the water flow duration in large Martian fluvial systems. Planetary and Space Science Journal, 163, 83-96. doi: 10.1016/j.pss.2018.06.001
Alemanno G.: 2018, Study of the fluvial activity on Mars through mapping, sediment transport modelling and spectroscopic analyses. PhD dissertation thesis, arXiv:1805.02208 [astro-ph.EP].

Spatial data from Schulp et al., 2014. Uncertainties in ecosystem service maps: A comparison on the European scale. PloS ONE 9, e109643. Safeguarding the benefits that ecosystems provide to society is increasingly included as a target in international policies. To support such policies, ecosystem service maps are made. However, there is little attention for the accuracy of these maps. We made a systematic review and quantitative comparison of ecosystem service maps on the European scale to generate insights in the uncertainty of ecosystem service maps and discuss the possibilities for quantitative validation. This data package contains maps of the ecosystem services climate regulation, erosion protection, flood regulation, pollination, and recreation. For each service, a map of the average supply according to all analyzed maps is included, as well as a map of the uncertainty of the service. The data package contains a detailed read-me.

ADMMR map collection: New Planet Iron Sections; 1 in. to 200 feet; 20 x 13 in.