This layer represents Land use polygons as determined by a combination of analytic techniques, mostly using Landsat 5 image mosaics . BTM 1 was done on a federal satellite image base that was only accurate to about 250m. The images were geo-corrected, not ortho-corrected, so there is distortion in areas of high relief. This is not a multipart feature

This map draws attention to your thematic content by providing a neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground. This light gray map supports any strong colors, creating a visually compelling map graphic which helps your reader see the patterns intended. This map was developed by Esri using HERE data, DeLorme basemap layers, OpenStreetMap contributors, Esri basemap data, and select data from the GIS user community. Worldwide coverage is provided from Level 0 (1:591M scale) through Level 13 (1:72k scale). In North America (Canada, Mexico, United States), Europe, India, South America and Central America, Africa, most of the Middle east, and Australia & New Zealand coverage is provided from Level 14 (1:36k scale) through Level 16 (1:9k scale). For more information on this map, including the terms of use, visit us online.

The datasets are in MID/MIF formats to be processed in QGIS with use of self-written open source software. The datasets are used to model single or multiple socio-economic scenarios of regional spatial development and to build graded suitability maps.

The datasets contain:

• the 10x10 km grid, topographic layers (navigable rivers, railways, paved roads, settlements, and river ports), and semantic description of each unit area of the Novosibirsk region;
• thematic maps (accessibility maps) on navigational rivers, paved roads, railways, river ports, and settlements.

These layers contain all of the MIT light grey basemap layers used for thematic mapping.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Cogan Technology, Inc. (CTI) created the digital vegetation map layer for the Mojave National Preserve and Castle Mountains National Monument project, which covered over 1.6 million acres (640,000 ha). In the accuracy assessment (AA) phase, contracted field crews collected data at 720 AA point locations that were randomly placed by map class throughout the project area. In the final phase, CTI finalized the classification, reported the AA results, revised the final vegetation map, and delivered the final products to the NPS VMI. The resulting spatial database and vegetation map layer were created using a combination of 2018 National Agriculture Imagery Program (NAIP) basemap data, ground-based verification efforts, and a two-step or hybrid mapping approach that used both manual and automated techniques. By comparing the vegetation signatures on the imagery to the field data, 87 map units (72 vegetated and 15 land-use/land-cover) were developed and used to delineate the plant communities. The interpreted vegetation polygons were then digitized into a Geographic Information System (GIS) layer that was field-tested, reviewed, and revised. The final MOJA and CAMO vegetation map layer was assessed for overall thematic accuracy at 91% with a Kappa value of 92%.

These layers contain all of the MIT light grey basemap layers used for thematic mapping.

This is a point dataset that reflects the locations of all existing libraries in Lexington-Fayette County. Points were identified utilizing public records and heads-up digitizing. Dataset was created for use in the LFUCG AtLex map book. Attributes include name and address.As part of the basemap data layers, the library location map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).The native projection for the data is Kentucky State Plane North (NAD83), but may have been reprojected for use in other applications. Please check metadata to determine current projection.

The city of Nürnberg (as many cities) recorded war damage on paper maps during WWII, using cadastral base maps. This dataset provides a vectorised representation of the 1942 cadastral map with building footprints as basic geo-features. These are enriched with data from thematic maps on heritage values and war damage.

Version 1 contains:

• The QGIS Project file
• A Geopackage file with the building footprint geometry layer, “Gebaeude_Nuernberg_Altstadt_V1”. On this layer the thematic information from the maps has been added as attribute values. Every building footprint feature has one value per attribute. Which means that in places where a building footprint contains more than one categorisation on a map this model only has the value of the most prominent categorisation. This reduction will be changed in later versions of the geopackage.
  There are also several layers without geometries. These hold the categorisation information from the historical maps, “Legend_X_265”. Additionally, the “Source_Overview” layer links map sources and attributes in the geometry layer.

Citation:

Please Cite the War Damage Atlas alongside the Dataset:

Enss, Carmen M. Atlas Kriegsschadenskarten Deutschland: Stadtkartierung und Heritage Making Im Wiederaufbau Um 1945. 1st ed. Basel/Berlin/Boston: Walter de Gruyter GmbH, 2023.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

To produce the spatial database and map layer, 2006, 0.6-meter, 4-band Quickbird satellite imagery (supplemented with 2008 Quickbird imagery) was provided by PACN. By comparing the signatures on the imagery to field and ground data 30 map units (18 vegetated, five barren, and seven land-use / land-cover) were developed and directly crosswalked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases and maps were printed, field tested, reviewed, and revised. The final map layer was accessed for thematic accuracy by overlaying 90 independent accuracy assessment points. The final overall accuracy of the map layer was determined to be 97% with a Kappa value of 82%.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the spatial database and map layer, year 2006 0.6-meter, 4-band Quickbird satellite imagery was provided by PACN. By comparing the signatures on the imagery to field and ground data 40 map units (23 vegetated, five barren or geologic, and 12 land-use / land-cover) were developed and directly crosswalked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases and maps were printed, field tested, reviewed, and revised. The final map layer was accessed for thematic accuracy by overlaying 77 independent accuracy assessment points. The final overall accuracy of the map layer was determined to be 86% with a Kappa value of 88%.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for American Memorial Park. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the spatial database and map layer, 0.6-meter, 4-band Quickbird satellite imagery from 2006 was provided by PACN. By comparing the signatures on the imagery to field and ground data 27 map classes (16 vegetated, three barren, and eight land-use / land-cover) were developed and directly crosswalked or matched to their corresponding NVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases and maps were printed, field tested, reviewed, and revised. The final map layer was accessed for thematic accuracy by overlaying 48 independent accuracy assessment points.

This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2019 that do not meet standard MTBS size criteria. These data are published to augment the data that are available from the MTBS program. This product was produced using the methods of the Monitoring Trends in Burn Severity Program (MTBS), however these fires do not meet the size criteria for a standard MTBS assessment. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. MTBS typically maps fires using an initial assessment (immediately after the fire) or an extended assessment (peak of green the season after the fire) for low-biomass and high-biomass fires respectively. Refer to MTBS.gov for more information on MTBS methods and criteria. Standard MTBS mappings must meet the size criteria of at least 500 acres for the eastern states and territories and 1,000 acres for the western states and territories to be eligible for mapping. Undersized MTBS fires are those fires that do not meet the standard MTBS size criteria but are otherwise mapped using standard MTBS methodologies.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. For PUHE, 31 map units (14 vegetated, five geologic, and 12 land-use/land-cover) were developed . The final list of map classes/units was directly crosswalked to corresponding plant associations and land use classes. PUHE map classes represent a compromise between the detail of the rUSNVC, resource management needs, and the limitations of the imagery. As a result, the mapping legend does not exactly match the rUSNVC. In most cases the rUSNVC and Park Special associations were used as map units. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases and maps were printed, field tested, reviewed, and revised. The final map layer was accessed for thematic accuracy by overlaying 57 independent accuracy assessment points. The final overall accuracy of the map layer was determined to be 97% with a Kappa value of 82%

As part of the basemap data layers, the waterbody boundary map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

This dataset is designed to represent and identify the boundaries of public school facilities within Lexington-Fayette County. The dataset is created by leveraging the appropriate boundaries in the GIS parcel dataset. The location of the public school facilities is updated through public record and coordination with the Fayette County Public School. The location for the certified private schools is updated through public record for certified private schools from the Kentucky Department of Education. The public school facilities are continuously updated. This dataset participates in a topology with the parcel dataset to assure coincident geometry during parcel editing.As part of the basemap data layers, the school boundary map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the spatial database and map layer, 2006, 0.6-meter, 4-band Quickbird satellite imagery (supplemented with 2008 Quickbird imagery) was provided by PACN. By comparing the signatures on the imagery to field and ground data 30 map units (18 vegetated, five barren, and seven land-use / land-cover) were developed and directly crosswalked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases and maps were printed, field tested, reviewed, and revised. The final map layer was accessed for thematic accuracy by overlaying 90 independent accuracy assessment points. The final overall accuracy of the map layer was determined to be 97% with a Kappa value of 82%.

This group layer provides a template for showing a thematic pattern based on where humans live. This is done using three things:The WorldPop Populated Footprint layer from ArcGIS Living Atlas. The legend and pop-up are disabled so that your thematic data is still the focus of the mapThe Destination In Blend Mode applied to the World Pop layerWhatever thematic layer (or layers) you want to apply the pattern to, underneath the WorldPop layer within the Table of ContentsThis combination of data sources provides a visualization that helps you see beyond administrative boundaries, and lets you see the pattern based on human settlement. To use this group layer template, add it to your map and replace the County layer with whatever layer (or layers) of thematic data you want. Make sure to keep your thematic data below the Populated Footprint layer in the Table of Contents. To do more with this, try these cartographic techniques:On the group layer itself (not the data layers), apply a Dropshadow Effect. This will make the human settlement pop. If it is too much, try applying 50% opacity to the dropshadow, or change the dropshadow color to something lighter. Duplicate your thematic layer(s) and pull the duplicate layer out of the group layer and underneath it within the Table of Contents. Then apply a 50-60% transparency to the layer. This will allow your data pattern to appear with the traditional boundaries behind the scenes, simpy as context. This blog shows a similar approach.