The Digital Geologic-GIS Map of Santa Monica Mountains National Recreation Area and Vicinity, 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 (samo_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 (samo_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 (samo_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 (samo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (samo_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 (samo_geology_metadata_faq.pdf). Please read the samo_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: California 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 (samo_geology_metadata.txt or samo_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 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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 Geologic-GIS Map of Fort Vancouver National Historic Site and Vicinity, Washington 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 (fova_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 3.X map file (.mapx) file (fova_geology.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 (fova_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (fova_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 (fova_geology_metadata_faq.pdf). Please read the fova_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: 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 (fova_geology_metadata.txt or fova_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 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).

This layer presents the Universal Transverse Mercator (UTM) zones of the world. The layer symbolizes the 6-degree wide zones employed for UTM projection.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World UTM Zones Grid.

Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.

Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the _location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Data fields Field Names Definitions USGS_ID Unique USGS identifier for each landslide entry. Date_Min Minimum possible date of landslide occurrence. If date is known to the day, Date_Min will have a value while Date_Max is empty. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Date_Max Maximum possible date of landslide occurrence. If date is known to the day, Date_Max will be empty while Date_Min has a value. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Fatalities Number of fatalities caused by landslide event. Confidence Confidence in landslide (entry) extent, nature, and _location. LS_Type Landslide (entry) type. Classification schema of original inventories is often not specified. Inventory Name of original source inventory. Inv_URL URL or link to original source inventory. Info_Source Information source or sub-layer from original source inventory. Notes Unformatted notes field, includes additional information. Lat_N Latitude of point or polygon centroid in WGS 1984 Lon_W Longitude of point or polygon centroid in WGS 1984 Confidence attributes Confidence Definitions 1 Possible landslide (feature) in the area 2 Probable landslide (feature) in the area 3 Likely landslide (feature) at or near this _location 5 Moderate confidence in extent or nature of landslide (feature) at this _location 8 High confidence in extent or nature of landslide (feature) References Belair, G.M., Jones, E.S., Slaughter, S.L., and Mirus, B.B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N. Cruden, D.M. and Varnes, D.J., 1996, Landslide Types and Processes, in Turner, K.A. and Schuster R. L., eds., Landslides Investigation and Mitigation: Transportation Research Board, U.S. National Research Council Special Report 247, U.S. National Academy of Sciences, Chapter 3, p. 36-75. ESRI, 2023, ArcGIS Pro (Version 3.1.3), Redlands, CA: Environmental Systems Research Institute, Retrieved from https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources. Hungr, O., Leroueil, S., and Picarelli, L., 2014, The Varnes classification of landslide types, an update, Landslides, 11(2), p. 167-194, https://doi.org/10.1007/s10346-013-0436-y. Jones, E.S., Mirus, B.B, Schmitt, R.G., Baum, R.L., Burns, W.J., Crawford, M., Godt, J.W., Kirschbaum, D.B., Lancaster, J.T., Lindsey, K.O., McCoy, K.E., Slaughter, S., and Stanley, T.A., 2019, Landslide Inventories across the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9E2A37P. Python Software Foundation, 2023, Python Language Reference, version 3.9, Retrieved from http://www.python.org. QGIS.org, 2022, QGIS Geographic Information System (Version 3.28.4-Firenze), QGIS Association, Retrieved from http://www.qgis.org.

Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars

Overview:

This repository contains the map-projected HiRISE Digital Elevation Models (DEMs) and the map-projected HiRISE image for each DEM and for each site in the study. Also contained in the repository is a GeoPackage file (beds_2019_08_28_09_29.gpkg) that contains the dip corrected bed thickness measurements, longitude and latitude positions, and error information for each bed measured in the study. GeoPackage files supersede shapefiles as a standard geospatial data format and can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS. For more information about GeoPackage files, please use https://www.geopackage.org/ as a resource. A more detailed description of columns in the beds_2019_08_28_09_29.gpkg file is described below in a dedicated section. Table S1 from the supplementary is also included as an excel spreadsheet file (table_s1.xlsx).

HiRISE DEMs and Images:

Each HiRISE DEM, and corresponding map-projected image used in the study are included in this repository as GeoTiff files (ending with .tif). The file names correspond to the combination of the HiRISE Image IDs listed in Table 1 that were used to produce the DEM for the site, with the image with the smallest emission angle (most-nadir) listed first. Files ending with “_align_1-DEM-adj.tif” are the DEM files containing the 1 meter per pixel elevation values, and files ending with “_align_1-DRG.tif” are the corresponding map-projected HiRISE (left) image. Table 1 Image Pairs correspond to filenames in this repository in the following way: In Table 1, Sera Crater corresponds to HiRISE Image Pair: PSP_001902_1890/PSP_002047_1890, which corresponds to files: “PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif” for the DEM file and “PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif” for the map-projected image file. Each site is listed below with the DEM and map-projected image filenames that correspond to the site as listed in Table 1. The DEM and Image files can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

· Sera

o DEM: PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif

o Image: PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif

· Banes

o DEM: ESP_013611_1910_ESP_014033_1910_align_1-DEM-adj.tif

o Image: ESP_013611_1910_ESP_014033_1910_align_1-DRG.tif

· Wulai 1

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Wulai 2

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Jiji

o DEM: ESP_016657_1890_ESP_017013_1890_align_1-DEM-adj.tif

o Image: ESP_016657_1890_ESP_017013_1890_align_1-DRG.tif

· Alofi

o DEM: ESP_051825_1900_ESP_051970_1900_align_1-DEM-adj.tif

o Image: ESP_051825_1900_ESP_051970_1900_align_1-DRG.tif

· Yelapa

o DEM: ESP_015958_1835_ESP_016235_1835_align_1-DEM-adj.tif

o Image: ESP_015958_1835_ESP_016235_1835_align_1-DRG.tif

· Danielson 1

o DEM: PSP_002733_1880_PSP_002878_1880_align_1-DEM-adj.tif

o Image: PSP_002733_1880_PSP_002878_1880_align_1-DRG.tif

· Danielson 2

o DEM: PSP_008205_1880_PSP_008930_1880_align_1-DEM-adj.tif

o Image: PSP_008205_1880_PSP_008930_1880_align_1-DRG.tif

· Firsoff

o DEM: ESP_047184_1820_ESP_039404_1820_align_1-DEM-adj.tif

o Image: ESP_047184_1820_ESP_039404_1820_align_1-DRG.tif

· Kaporo

o DEM: PSP_002363_1800_PSP_002508_1800_align_1-DEM-adj.tif

o Image: PSP_002363_1800_PSP_002508_1800_align_1-DRG.tif

Description of beds_2019_08_28_09_29.gpkg:

The GeoPackage file “beds_2019_08_28_09_29.gpkg” contains the dip corrected bed thickness measurements among other columns described below. The file can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

(Column_Name: Description)

sitewkn: Site name corresponding to the bed (i.e. Danielson 1)

section: Section ID of the bed (sections contain multiple beds)

meansl: The mean slope (dip) in degrees for the section

meanaz: The mean azimuth (dip-direction) in degrees for the section

ang_error: Angular error for a section derived from individual azimuths in the section

B_1: Plane coefficient 1 for the section

B_2: Plane coefficient 2 for the section

lon: Longitude of the centroid of the Bed

lat: Latitude of the centroid of the Bed

thickness: Thickness of the bed BEFORE dip correction

dipcor_thick: Dip-corrected bed thickness

lon1: Longitude of the centroid of the lower layer for the bed (each bed has a lower and upper layer)

lon2: Longitude of the centroid of the upper layer for the bed

lat1: Latitude of the centroid of the lower layer for the bed

lat2: Latitude of the centroid of the upper layer for the bed

meanc1: Mean stratigraphic position of the lower layer for the bed

meanc2: Mean stratigraphic position of the upper layer for the bed

uuid1: Universally unique identifier of the lower layer for the bed

uuid2: Universally unique identifier of the upper layer for the bed

stdc1: Standard deviation of the stratigraphic position of the lower layer for the bed

stdc2: Standard deviation of the stratigraphic position of the upper layer for the bed

sl1: Individual Slope (dip) of the lower layer for the bed

sl2: Individual Slope (dip) of the upper layer for the bed

az1: Individual Azimuth (dip-direction) of the lower layer for the bed

az2: Individual Azimuth (dip-direction) of the upper layer for the bed

meanz: Mean elevation of the bed

meanz1: Mean elevation of the lower layer for the bed

meanz2: Mean elevation of the upper layer for the bed

rperr1: Regression error for the plane fit of the lower layer for the bed

rperr2: Regression error for the plane fit of the upper layer for the bed

rpstdr1: Standard deviation of the residuals for the plane fit of the lower layer for the bed

rpstdr2: Standard deviation of the residuals for the plane fit of the upper layer for the bed

The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP will be the NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards.

The first public release of the 3D Hydrography Program map service may be accessed at https://hydro.nationalmap.gov/arcgis/rest/services/3DHP_all/MapServer.

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

Administrative Boundaries of the Russian Empire (1820s): Kingdoms & Grand Duchy, as depicted on the Geographical Atlas of the Russian Empire produced by the Military-Topographical Depot of His Imperial Majesty's General Staff, 1820-1827. Component of the Imperiia Project. Documentation and analysis available here (http://dighist.fas.harvard.edu/projects/imperiia/items/show/642)

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.

Producto cartográfico básico a escala 1:1.000 que contiene: a) Elementos altimétricos, los cuales se obtienen a partir de procesos fotogramétricos o técnicas de interferometría. Intervalo básico de curvas de nivel cada 1 metro. Curvas de nivel índice cada 5 metros. b) Elementos planimétricos, obtenidos desde procesos fotogramétricos o fotointerpretación, los cuales son estructurados en una base de datos en formato Geodatabase, conforme al modelo de datos vigente de producción cartográfica. Se captura los elementos para la escala de carácter permanente, hasta el límite determinado para el proyecto. c) La Cabecera Municipal de Junín, tiene un cubrimiento aproximado de 34,494 hectáreas. d) El proceso se realizó con Fotografías Aéreas de:20211020, Compilación Toponímica insumo de: 2019y 2021, Restitución Fotogramétrica en: 2021.

GnoIDE garantiza la disponibilidad de recursos tecnológicos específicos para que las administraciones que no los poseen, puedan compartir información geográfica y centren sus esfuerzos en el mantenimiento de los conjuntos de datos de los que son competentes y que la legislación señala como obligatoria su publicación.

Es el Instituto de Estadística y Cartografía de Andalucía (IECA) quien pone a disposición su infraestructura tecnológica de servidores, software de servidor de aplicaciones, bases de datos o programas específicos para publicar mapas (GeoServer) o metadatos (Geonetwork), de acuerdo a estándares internacionales que garantizan su interoperabilidad.

Los usuarios externos al IECA solo necesitan conectarse a una aplicación web desde donde cargar sus capas de información espacial, configurar una leyenda y publicar mapas como servicios interoperables. Una vez compartidos los servicios de mapas, pueden visualizarse directamente en la aplicación, o en infinidad de visores web o clientes de escritorio como QGIS, GVSIG o ArcGis, ya que todos tienen la capacidad de consumir los servicios estandarizados generados por GnoIDE.

This dataset comprises land use maps of Maputo city, with exception of the KaTembe urban district, for the years 1964, 1973, 1982, 1991 and 2001. It is the digital version of the land use maps published by Henriques [1] and revised under the LUCO research project.

The land use of Maputo city was identified from: i) aerial photographs (1964, 1982, 1991), orthophoto maps (1973) and IKONOS images (2001); ii) documentary sources, such as the Urbanization Master Plan (1969) and the Maputo City Addressing (1997); iii) the recognition made during several field survey campaigns. The methodology is described in Henriques [1].

Land use was classified into three levels, resulting from a hierarchical classification system, including descriptive and parametric classes. Levels I and II are available in this repository.

Level I, composed by 10 classes, contains the main forms of occupation: built-up areas (residential, economic activity, equipment, and infrastructure) and non-built-up areas (vacant or "natural"). It is geared towards analyses that serve policymaking and resource management at the regional or national scale [1].

Level II, composed by 31 classes, discriminates the higher hierarchical level according to its functional land use to become useful for municipal planning and management in municipal master plans, for example [1].

Maps are available in shapefile format and include predefined symbology-legend files, for QGIS and ArcGIS (v.10.7 or higher). The urban land use classes are described in Portuguese and English, and their meaning is provided as an accompanying document (ULU_Maputo_Nomenclatura_PT.pdf / ULU_Maputo_Nomenclature_EN.pdf).

Data format: vector (shapefile, polygon)

Reference system: WGS84, UTM 36S (EPSG:32736)

Original minimum mapping unit: 25 m2

Urban Land Use dataset attributes:

[N_I_C] – code of level I

[N_I_D_PT] – name of level I, in Portuguese

[N_I_D_EN] - name of level I, in English

[N_II_C] – code of level II

[N_II_D_PT] - name of level II, in Portuguese

[N_II_D_EN] - name of level II, in English

Funding: this research was supported by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P. Project number: FCT AGA-KHAN/ 541731809 / 2019

[1] Henriques, C.D. (2008). Maputo. Cinco décadas de mudança territorial. O uso do solo observado por tecnologias de informação geográfica [Maputo. Five decades of territorial transformation. Land use assessed by geographical information technologies]. Lisboa, Instituto Português de Apoio ao Desenvolvimento (ISBN: 978-972-8975-22-7).

Il presente progetto Qgis riunisce i quattro progetti realizzati per la redazione delle carte delle quattro invarianti di supporto al quadro conoscitivo delle quattro invarianti individuate nell’ambito delle attivita' di revisione della disciplina paesaggistica del PIT della Regione Toscana. I progetti originari sono stati concepiti in funzione della successiva realizzazione di altrettante carte in formato raster. Il presente progetto riunisce e in parte semplifica i progetti originari eliminando le duplicazioni di dati a comune, ecc. I set di dati utilizzati nel progetto, in formato shape, non vengono documentati singolarmente in quanto funzionali alla realizzazione di carte raster ciascuna delle quali si autoesplica tramite legenda propria e specifico metadato, a cui peraltro si rimanda per ulteriori informazioni (vedi Download risorse: http://bit.ly/1GBJkjL). Va ricordato che i risultati di eventuali elaborazioni vettoriali, in ambiente gis, dei dati del progetto, vanno utilizzati alle scale originali delle carte raster da essi derivate (1:50.000 e 1:250.000).Il progetto si compone dei seguenti strati tematici:Quadro d’unione 50K – Quadro d’unione dei fogli della Carta d’Italia 1:50.000 dell’IGM sulla base del quale sono stati ripartiti i fogli delle carte delle invarianti I, II, III. Ambiti comunali – Limiti comunali Ambiti di paesaggio – Limiti degli Ambiti di paesaggio individuati dal Piano paesaggistico regionaleInvariante I - Carta dei sistemi morfogenetici - Cartografia in scala 1:50.000, estesa all’intero territorio della Regione Toscana, finalizzata alla rappresentazione dei caratteri morfogenetici del territorio toscano, ovvero degli elementi obiettivamente riconoscibili della struttura fisica del paesaggio, definiti da una combinazione dei fattori che presiedono allo sviluppo delle forme del rilievo quali i fattori strutturali, temporali e geologici. Invariante II – Carta della rete ecologica - Cartografia in scala 1:50.000, estesa all’intero territorio della regione Toscana, finalizzata alla evidenziazione degli elementi strutturali e funzionali della rete ecologica regionale. La carta e' concepita per rappresentare il livello di frammentazione ecologica alla scala regionale, i nuclei sorgente di biodiversita' sia per gli ecosistemi forestali che per quelli agricoli e pastorali, la matrice di connettivita' nonche' gli elementi critici per la funzionalita' della rete. Invariante III – Carta del territorio urbanizzato - Cartografia in scala 1:50.000, estesa all’intero territorio della regione Toscana. L’associazione degli attributi riferiti alla periodizzazione edilizia permette una serie di elaborazioni volte alla esclusiva rappresentazione della crescita degli insediamenti o della rappresentazione dell’edificato ad una data soglia temporaleInvariante IV – Carta dei morfotipi rurali - Cartografia in scala 1:250.000, estesa all’intero territorio della regione Toscana. La carta mira a fornire una rappresentazione in forma di areali dei tipi di paesaggio rurale presenti nella regione, intesi come forme riconoscibili derivanti dall'incrocio di diversi fattori quali quelli geomorfologici, insediativi, naturalistici, colturali.

Cobertura que representa los límites administrativos a nivel regional de Chile. Información vectorial de tipo polígono.

Qui potete trovare una struttura IS 50V che è 1 strato di 8 in IS 50V, https://www.lmi.is/is/landupplysingar/gagnagrunnar/is-50v/mannvirki e https://gatt.lmi.is/geonetwork/srv/eng/catalog.search#/metadata/4C05F02C-7095-4C65-97A1-AC91AE427664.

I dati possono essere consultati nel portale geografico LMÍ https://kort.lmi.is/.

Per utilizzare i dati è necessario un software speciale (ad esempio QGIS, ArcGIS, GRAS GIS, qvGIS, Opticks, Microstation o Autodesk).

Geological Maps and Digital Data contains outlines illustrating areas that have published products released by the Ontario Geological Survey.

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Geology Terrain contains an evaluation of near-surface geological conditions such as material, landform, topography and drainage.

Producto cartográfico básico a escala 1:1.000 que contiene: a) Elementos altimétricos, los cuales se obtienen a partir de procesos fotogramétricos o técnicas de interferometría. Intervalo básico de curvas de nivel intermedias cada 1 metro. Curvas de nivel índice cada 5 metros. b) Elementos planimétricos, obtenidos desde procesos fotogramétricos o fotointerpretación, los cuales son estructurados en una base de datos en formato Geodatabase, conforme al modelo de datos vigente de producción cartográfica. Se captura los elementos para la escala de carácter permanente, hasta el límite determinado para el proyecto. c) La Cabecera Municipal de Guachetá, tiene un cubrimiento aproximado de 88,07 hectáreas. d) El proceso se realizó con imágenes provenientes del sensor SONY a bordo de un vehículo aéreo no tripulado (UAV) del día 17 de septiembre de 2022. Compilación Toponímica insumo de: 2014, 2020. Restitución Fotogramétrica en: 2022.