The SarcDEM_As_Tif is a zip file containing a 5ft x 5ft Digital Elevation Model for Sarasota County, FL stored in TIF image file format. Includes a tif world file for georegistration and an pyramid file. Suitable for use in AutoCAD products. The DEM was developed from an ESRI Terrain Dataset comprised of mass points (average 4ft spacing), 2-D and 3-D breakline features, and a softclip boundary footprint. The mass points and breaklines were compiled in 2007 to support of the the Florida Division of Emergency Management (FDEM) development and maintenance of Regional Evacuation (Storm Surge) Studies.To account for roadway, housing and commercial development since 2007, updates to the masspoints and breaklines were applied in 2016 for the following watersheds:Phillipi Creek, Little Sarasota Bay, Lemon Bay and Donna Roberts Bay.To download click HERE and select 'Download' from the upper-right button

India's elevation data as a single TIFF file. See https://github.com/dilawar/map-india-center for more details.MD5 checksum: 97dcbee8b20f3b4de3036cfb9701a5e7 india.clipped.tif# CreditsFile india-composite.geojson is from datameet repository https://github.com/datameet/maps/tree/master/Country (Release under http://creativecommons.org/licenses/by-sa/2.5/in/ )

NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical mmap of the NW suburbs of the Twin Cities.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

Using data collected in 2020, The City has created this solar potential dataset of all buildings within city limits. The data shows varying degrees of a roof’s solar exposure, on an annual basis, in generalized optimal conditions. The data model used to generate the map takes into account the shape of the terrain and the relative position of building rooftops and structures, existing infrastructure, and tree canopies. It doesn’t take into account weather conditions, such as cloudy days and precipitation that limit a roof’s direct solar exposure. It also does not reflect any new adjacent structures captured after 2020 that may obstruct another building’s solar exposure.

The Yield levels are measured as kWh\m2\day. Yield values are interpreted as: Low Yield (0 - 0.87); Low Moderate Yield (0.87 - 1.74); High Moderate Yield (1.74 - 2.61); High Yield (2.61 - 3.48)

The compressed file for download is 362 MB. The uncompressed files are 4.46 GB. Data is arranged in Townships, with 21 GeoTiff image files in total.

The Solar map data is intended for information purposes only and as a preliminary solar opportunity assessment tool. It is not intended to be used as a decision making source of information for solar panel installations.

TitleMap of eastern Labrador : showing Grand Lake and the courses of the Nascaupee and George Rivers as surveyed and mapped June 27 to August 27, 1905 ; with the Susan and Big Rivers showing the route of Mr. Leonidas Hubbard, Jr. in the summer of 1903SubjectHubbard, Leonidas, 1872-1903Explorers--Newfoundland and Labrador--Labrador--MapsLabrador (N.L.)--Discovery and exploration--MapsScale1:1,584,000, 25 statute miles to 1 inchCoordinates[W 69° - W 57° / N 59° - N 52°]DescriptionColour. Relief shown by hachures, spot heights and text. Shows camps and portages with dates. -- Inset : Nascaupee and George Rivers as they appeared on maps in 1905 (from Stieler's Hand Atlas). Scale 1:7 500 000. "True course of Nascaupee & George Rivers shown in red" (title box).CreatorHubbard, MinaPlace of Publication[London]Publisher[John Murray]Date[1908]Dimensions of Original50 x 48 cmLocationCanada--Newfoundland and Labrador--LabradorTime Period20th CenturyLanguageengLocal Call NumberG 3441 A85 1905 H82 MAPTypeStill ImageResource TypeMapFormatimage/jpegRelationAccompanies: Hubbard, Mina. A woman's way through unknown Labrador : an account of the exploration of the Nascaupee and George River (Toronto : W. Briggs ; London : J. Murray, 1908). Located in CNS under call number FF 1041 E47 1908 RARE, and available on the Digital Archive at http://134.153.184.110/cdm/ref/collection/cns/id/51115CollectionDigitized Maps from the Centre for Newfoundland StudiesSponsorCentre for Newfoundland StudiesSourcePrint map held in the Centre for Newfoundland Studies.RepositoryMemorial University of Newfoundland. Libraries. Centre for Newfoundland StudiesHigh ResolutionRight click link to download jpeg (20 MB) http://collections.mun.ca/maps/MapOfEasternLabrador.jpg -- Right click link to download tif file (49.7 MB) http://collections.mun.ca/maps/MapOfEasternLabrador.tifCONTENTdm file name260.jp2

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.]

TitleMap of LabradorSubjectLabrador (N.L.)--MapsLabrador (N.L.)--Boundaries--MapsQuébec (Province)--Boundaries--MapsScale[c.1:3,168,000]Coordinates[W 83° - W 55° / N 61° - N 50°]DescriptionBlack and white. Prime meridian Greenwich. Compass rose. Bar scale in statute miles. "To illustrate the report of A.P. Low, B.A.Sc. Reduced from field plotting subject to correction (Signed D.J.V. Eaton)" (title).CreatorGeological Survey of CanadaPlace of Publication[Ottawa]PublisherGeological Survey of CanadaDate[ca. 1894]Dimensions of Original46.2 x 61.7 cmContributorsLow, A. P. (Albert Peter), 1861-1942Eaton, D. J. V.LocationCanada--Newfoundland and Labrador--LabradorCanada--Québec (Province)Time Period19th CenturyLanguageengNotesCNS map no. 248.Local Call NumberG 3440 1894 G4 MAPTypeStill ImageResource TypeMapFormatimage/jpegRelationAccompanies: Quebec (Province). Legislature. Legislative Assembly. Return to an address of the Legislative Assembly... respecting the north and north-east frontiers of the Province of Quebec. This report is held in the CNS Rare collection, call no. F 1054 B7 Q4 1897 RARE.CollectionDigitized Maps from the Centre for Newfoundland StudiesSponsorCentre for Newfoundland StudiesSourcePrint map held in the Centre for Newfoundland Studies.RepositoryMemorial University of Newfoundland. Libraries. Centre for Newfoundland StudiesHigh ResolutionRight click link to download jpeg (11.4 MB) http://collections.mun.ca/maps/G_3440_1894_G4.jpg -- Right click link to download tif file (263 MB) http://collections.mun.ca/maps/G_3440_1894_G4.tifCONTENTdm file name58.jpg

The Louisiana Aeromagnetic Map compilation provided by the Louisiana Geological Survey is one part of a national digital compilation by the USGS. The scanned Louisiana Aeromagnetic Map is geo-referenced TIFF file published as a Web map service, ESRI service endpoint, and a geo-referenced TIFF file for download. This resource was provided by the Louisiana Geological Survey and made available for distribution through the National Geothermal Data System Project.

Welcome to the LandsatLook Viewer!The LandsatLook Viewer is a prototype tool that was developed to allow rapid online viewing and access to the USGS Landsat image archives. This viewer allows you to:Interactively explore the Landsat archive at up to full resolution directly from a common web browserSearch for specific Landsat images based on area of interest, acquisition date, or cloud coverCompare image features and view changes through timeDisplay configurable map information layers in combination with the Landsat imageryCreate a customized image display and export as a simple graphic fileView metadata and download the full-band source imagerySearch by address or place, or zoom to a point, bounding box, or Sentinel-2 Tile or Landsat WRS-1 or WRS-2 Path/RowGenerate and download a video animation of the oldest to newest images displayed in the viewerWe welcome feedback and input for future versions of this Viewer! Please provide your comments or suggestions .About the ImageryThis viewer provides visual and download access to the USGS LandsatLook "Natural Color" imageproduct archive.BackgroundThe Landsat satellites have been collecting multispectral images of Earth from space since 1972. Each image contains multiple bands of spectral information which may require significant user time, system resources, and technical expertise to obtain a visual result. As a result, the use and access to Landsat data has been historically limited to the scientific and technical user communities.The LandsatLook “Natural Color” image product option was created to provide Landsat imagery in a simple user-friendly and viewer-ready format, based on specific bands that have been selected and arranged to simulate natural color. This type of product allows easy visualization of the archived Landsat image without any need for specialized software or technical expertise.LandsatLook ViewerThe LandsatLook Viewer displays the LandsatLook Natural Color image product for all Landsat 1-8 images in the USGS archive and was designed primarily for visualization purposes.The imagery within this Viewer will be of value to anyone who wants to quickly see the full Landsat record for an area, along with major image features or obvious changes to Earth’s surface through time. An area of interest may be extracted and downloaded as a simple graphic file directly through the viewer, and the original full image tile is also available if needed. Any downloaded LandsatLook image product is a georeferenced file and will be compatible within most GIS and Web mapping applications.If the user needs to perform detailed technical analysis, the full bands of Landsat source data may also be accessed through direct links provided on the LandsatLook Viewer.Image ServicesThe imagery that is visible on this LandsatLook Viewer is based on Web-based ArcGIS image services. The underlying REST service endpoints for the LandsatLook imagery are available at https://landsatlook.usgs.gov/arcgis/rest/services/LandsatLook/ImageServer .Useful linksLandsat- Landsat Mission (USGS)- Landsat Science (NASA)LandsatLook- Product Description- USGS Fact Sheet- LandsatLook image services (REST)Landsat Products- Landsat 8 OLI/TIRS- Landsat 7 ETM+- Landsat 4-5 TM- Landsat 1-5 MSS- Landsat Band DesignationsLandsatLook images are full-resolution files derived from Landsat Level-1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. It is recommended that these images not be used in image analysis.LandsatLook image files are included as options when downloading Landsat scenes from EarthExplorer, GloVis, or the LandsatLook Viewer (See Figure 1).Figure 1. LandsatLook and Level-1 product download optionsLandsatLook Natural Color ImageThe LandsatLook Natural Color image is a .jpg composite of three bands to show a “natural” looking (false color) image. Reflectance values were calculated from the calibrated scaled digital number (DN) image data. The reflectance values were scaled to a 1-255 range using a gamma stretch with a gamma=2.0. This stretch was designed to emphasize vegetation without clipping the extreme values.Landsat 8 OLI = Bands 6,5,4Landsat 7 ETM+ and Landsat 4-5 TM = Bands 5,4,3Landsat 4-5 MSS = Bands 2,4,1Landsat 1-3 MSS = Bands 7,5,4LandsatLook Thermal ImageThe LandsatLook Thermal image is a one-band gray scale .jpg image that displays thermal properties of a Landsat scene. Image brightness temperature values were calculated from the calibrated scaled digital number (DN) image data. An image specific 2 percent clip and a linear stretch to 1-255 were applied to the brightness temperature values.Landsat 8 TIRS = Band 10Landsat 7 ETM+ = Band 61-high gainLandsat 4-5 TM = Band 6Landsat 1-5 MSS = not availableLandsatLook Quality ImageLandsatLook Quality images are 8-bit files generated from the Landsat Level-1 Quality band to provide a quick view of the quality of the pixels within the scene to determine if a particular scene would work best for the user's application. This file includes values representing bit-packed combinations of surface, atmosphere, and sensor conditions that can affect the overall usefulness of a given pixel. Color mapping assignments can be seen in the tables below. For each Landsat scene, LandsatLook Quality images can be downloaded individually in .jpg format, or as a GeoTIFF format file (_QB.TIF) within the LandsatLook Images with Geographic Reference file.Landsat Collection 1 LandsatLook 8-bit Quality Images DesignationsLandsat 8 OLI/TIRSLandsat 7 ETM+, Landsat 4-5 TMLandsat 1-5 MSSColorBitDescriptionBitDescriptionBitDescription 0Designated Fill0Designated Fill0Designated Fill 1Terrain Occlusion1Dropped Pixel1Dropped Pixel 2Radiometric Saturation 2Radiometric Saturation ​2Radiometric Saturation 3Cloud3Cloud3Cloud 4Cloud Shadow4Cloud Shadow 4Unused 5Snow/Ice 5Snow/Ice 5Unused 6Cirrus 6Unused6Unused 7Unused7Unused7UnusedUnusedTable 1. Landsat Collection 1 LandsatLook 8-bit Quality Images Designations LandsatLook Images with Geographic ReferenceThe LandsatLook Image with Geographic Reference is a .zip file bundle that contains the Natural Color, Thermal, and the 8-bit Quality images in georeferenced GeoTiff (.TIF) file format.Figure 2. LandsatLook Natural Color Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 3. LandsatLook Thermal Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 4. LandsatLook Quality Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013 with background color set to dark grey. Additional Information About LandsatLook ImagesMany geographic information systems and image processing software packages easily support .jpg images. To create these files, Landsat data is mapped to a 1-255 range, with the fill area set to zero (if a no-data value is set to zero, the compression algorithm may introduce zero-value artifacts into the data area causing very dark data values to be displayed as no-data).

Supervisorial District Boundaries (1971) as derived from a scanned map. This layer was developed for informational and referential purposes, and for general investigation and analysis of district histories. The data may also be used for general cartographic purposes. It should not be used for legal questions.The data was digitized from scanned and rectified County Engineer maps provided by the Department of Public Works.Click here to view and download the scanned/rectified map as a .tif file

The confirmed town plan of Espoo includes the valid plan symbol at each location. However, the exact meanings of the plan symbols vary between town plans. Therefore, the reader of the confirmed town plan must find out which town plan is valid in the area and read the plan regulations of the town plan, which can also be found in the Espoo Map Service.

The confirmed town plan (combination of town plans) of Espoo is not suitable for dimensional calculations. The position of the construction area boundary has been interpreted graphically. For projects requiring high position accuracy, it is advisable to inquire about other data maintained by the Public Works Department.

It is always advisable to check the plot-specific symbols in the effective town plan. In spite of the proofreading, there is the possibility of human error in the production process of the confirmed plan. Do not design buildings without checking the data in the original plan. The City of Espoo is not responsible for any damage caused by failure to check the data.

WMS interface

The confirmed town plan includes the valid plan symbols. The map is available in black and white and colour. The dataset is position-accurate with the graphical accuracy of the original town plan and suitable for display at scales from 1:1,000 to 1:4,000.

API address:

• https://kartat.espoo.fi/teklaogcweb/wms.ashx

Layers:

• Ajantasa-asemakaava, musta
• Ajantasa-asemakaava, värillinen

File download

The confirmed town plan can be downloaded as 1 km * 1 km map tiles in TIFF file format. TIFF is a raster (image) file format that is displayed by geographic data programs and some other programs. The file is a so-called geoTIFF, which contains information about the position of the map tile in the GK25 coordinate system. The timeliness of the data corresponds to the confirmed town plan that can be viewed in the map service at the time of loading.

To download dataset, select the data by ticking it in the map layer menu on the left side of the Map Service (under “Open data”) and then clicking on the map window and the “Additional information” link in the speech bubble. The up-to-dateness of the downloaded data is indicated in the speech bubble.

The Louisiana Bouguer Gravity Map compilation provided by the Louisiana Geological Survey is one part of a national digital compilation by the USGS. The scanned Louisiana Bouguer Gravity Map is geo-referenced TIFF file published as a Web map service, ESRI service endpoint, and a geo-referenced TIFF file for download. This resource was provided by the Louisiana Geological Survey and made available for distribution through the National Geothermal Data System Project.

Supervisorial District Boundaries (1981) as derived from a scanned map. This layer was developed for informational and referential purposes, and for general investigation and analysis of district histories. The data may also be used for general cartographic purposes. It should not be used for legal questions.The data from was digitized from scanned and rectified County Engineer maps provided by the Department of Public Works.Click here to view and download the scanned/rectified map as a .tif file

This dataset contains the global raster files (at 250 m resolution) associated with a manuscript that has been accepted at the journal Geology:The unexpected global distribution of Earth's sediment sources and sinksHarrison K. Martin1,* and Michael P. Lamb11Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, 91125, U.S.A.*hkm@caltech.eduThe paper describes a spatially continuous high-resolution (250 meter) global map of sediment source, bypass, and sink domains. If you use the data in your own research or projects, please include references to the paper above and to this dataset.This repository contains three main items: 1) the global raster map, 2) MATLAB code used to create the map, and 3) reduced intermediate data designed to work with the MATLAB code, so that users can recreate or modify the map locally without downloading and processing all of the original input data. The three items are described below. Most users will only need to download the global map (1).Item (1), the global map, is found in the .zip file: "mask_strat_241022.zip"Items (2) and (3), the MATLAB code and intermediate data files, are found in the .zip file: "Source-to-sink Map EE 241022 public.zip"1) Global raster map: The dataset consists of 60 GeoTIFF tiles, each 12,000 pixels by 12,000 pixels (or fewer for edge tiles). Each pixel is 250 meters by 250 meters. Tiles are in the WGS 84 / Equal Earth Greenwich projection (https://epsg.io/8857). For convenience, also included is a .vrt (Virtual Raster) file, which can be opened in your GIS software of choice to load all tiles at once. Tiles are saved as .tif files containing 8-bit integer values, and are compressed using the PackBits algorithm. This substantially reduces the filesize of the resulting dataset without any loss of information.This dataset was created using a combination of QGIS and Matlab, and the method is described in the supporting information of the above manuscript.Pixel values are as follows:0: Ocean (can be set as the noData value in your GIS software for easier visualization)1: Sink2: Bypass3: Source4: Missing Data2) MATLAB code:This code can be run to reproduce our results. It comes in a folder with three subdirectories used to read the inputs and write TIF raster outputs (same as (1) above) and, optionally, PNGs. There is also a .txt file in there with instructions to run the code. I tried to make it as simple as possible to run. I also tried to design it with scientific computing in mind, i.e., able to be run in reasonable time by lower performance computers. Considering it's making a global map, the memory requirements are fairly small. On my computer, it takes less than ten minutes to reproduce the global map.3) Intermediate files:A folder containing ten tiled intermediate datasets, described in the Supplemental Information of the Geology manuscript. This is the input that the MATLAB code in (2) reads. These files go into the "VRTs" folder in the same directory as the MATLAB code. These files are all standardized, compressed, tiled, rasterized at the right resolution and in the right CRS, etc. This folder, including the code, instructions, and intermediate files, is zipped to 188 MB compared to >5.5 GB if users were to download the original datasets themselves. Please feel free to reach out with any questions!- Harrison MartinPostdoctoral Scholar Research Associate in GeologyCaltechJune 24 2025hkm@caltech.eduhttps://harrison.studies.rocksEDIT (25/06/24): Made repository public, uploaded the code and intermediate files, and expanded the description accordingly.

Overview

A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution.

The download contains the following files:

ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file

ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file

training.gpkg: training samples with class labels

LICENSE.pdf: NICFI data program user license

Map accuracy

We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types:

Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.

Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.

Further resources

The production of this map was made possible through the NICFI data program, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the NICFI data program license agreement included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at https://github.com/philipperufin/eepypr/.

We advise map users to read the preprint or the open access paper for detailed insights. In case of questions please consult these resources or contact the lead author of the work.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Raster files with the information of the Provincial Map 1:200,000. The download unit is a ZIP file for each province (except the Basque Country, whose three provinces are published together) that contains the TIF + TFW file. ED50 geodetic reference system (REGCAN95 in the Canary Islands) and UTM projection in the corresponding zone