The geoBoundaries Global Database of Political Administrative Boundaries Database is an online, open license resource of boundaries (i.e., state, county) for every country in the world. Currently 199 total entities are tracked, including all 195 UN member states, Greenland, Taiwan, Niue, and Kosovo. Comprehensive Global Administrative Zones (CGAZ) is a set of global composites for administrative boundaries. Disputed areas are removed and replaced with polygons following US Department of State definitions. It has three boundary levels ADM0, ADM1, and ADM2, clipped to international boundaries (US Department of State), with gaps filled between borders. This dataset is part of CGAZ. It was ingested from version 6.0.0 of Global Composite Files with DBF_DATE_LAST_UPDATE=2023-09-13. It shows boundaries at level ADM0 (country-level boundaries).

This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine, we examined inundation dynamics in California croplands from 2003 –2020 by intersecting monthly surface water maps (n=216 months) with mapped locations of precipitation amounts, rice, field, truck (which comprises truck, nursery, and berry crops), deciduous (deciduous fruits and nuts), citrus (citrus and subtropical), vineyards, and young perennial crops. Surface water maps were produced using the Dynamic Surface Water Extent (DSWE) model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating the monthly occurrence of “high confidence” water from each satellite collection across eight cropland types and 58 counties. The resulting tabular data have been joined to a county GIS shapefile covering the state of California. The file includes attributes summarizing each crop contained within the county boundaries along with a summary of how much cropland intersects past locations of cropland inundation, the relative percentage of cropland inundated, and the frequency of crop inundation. These summaries were generated using both the Landsat and MODIS water inundation maps, and are presented separately in the data release.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The City Boundary layer, originally created by the Office of City Clerk, bounds the City of Miami to its current limits. The layer is edited and maintained by GIS Team of the City. This layer is a representation of the official City of Miami Boundary Legal Description based on the legal survey of the city, but it is not to be construed as an official survey of the city boundary. It may be used for general reference purposes but not in place of an official survey or other legal instrument.

There is no description for this dataset.

Ricefield Instance

## Overview

Ricefield Instance is a dataset for instance segmentation tasks - it contains Rice Field annotations for 381 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries.
This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset.
This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose.
Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata.
To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only.
This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services.
See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

KML file of boundary for the city of Chicago. To view or use these files, special GIS software, such as Google Earth, is required.

Fast flood extent monitoring with SAR change detection using Google Earth Engine This dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliable tool for assisting decision-makers and emergency planners using near real-time multi-temporal satellite SAR data sets. GEE code was developed by Ebrahim Hamidi and reviewed by Brad G. Peter; Figures were created by Brad G. Peter. This tool accompanies a publication Hamidi et al., 2023: E. Hamidi, B. G. Peter, D. F. Muñoz, H. Moftakhari and H. Moradkhani, "Fast Flood Extent Monitoring with SAR Change Detection Using Google Earth Engine," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3240097. GEE input datasets: Methodology flowchart: Sensitivity Analysis: GEE code (muti-source and multi-temporal flood monitoring): https://code.earthengine.google.com/7f4942ab0c73503e88287ad7e9187150 The threshold sensitivity analysis is automated in the below GEE code: https://code.earthengine.google.com/a3fbfe338c69232a75cbcd0eb6bc0c8e The above scripts can be run independently. The threshold automation code identifies the optimal threshold values for use in the flood monitoring procedure. GEE code for Hurricane Harvey, east of Houston Java script: // Study Area Boundaries var bounds = /* color: #d63000 */ee.Geometry.Polygon( [[[-94.5214452285728, 30.165244882083663], [-94.5214452285728, 29.56024879238989], [-93.36650748443218, 29.56024879238989], [-93.36650748443218, 30.165244882083663]]], null, false); // [before_start,before_end,after_start,after_end,k_ndfi,k_ri,k_diff,mndwi_threshold] var params = ['2017-06-01','2017-06-15','2017-08-01','2017-09-10',1.0,0.25,0.8,0.4] // SAR Input Data var before_start = params[0] var before_end = params[1] var after_start = params[2] var after_end = params[3] var polarization = "VH" var pass_direction = "ASCENDING" // k Coeficient Values for NDFI, RI and DII SAR Indices (Flooded Pixel Thresholding; Equation 4) var k_ndfi = params[4] var k_ri = params[5] var k_diff = params[6] // MNDWI flooded pixels Threshold Criteria var mndwi_threshold = params[7] // Datasets ----------------------------------- var dem = ee.Image("USGS/3DEP/10m").select('elevation') var slope = ee.Terrain.slope(dem) var swater = ee.Image('JRC/GSW1_0/GlobalSurfaceWater').select('seasonality') var collection = ee.ImageCollection('COPERNICUS/S1_GRD') .filter(ee.Filter.eq('instrumentMode', 'IW')) .filter(ee.Filter.listContains('transmitterReceiverPolarisation', polarization)) .filter(ee.Filter.eq('orbitProperties_pass', pass_direction)) .filter(ee.Filter.eq('resolution_meters', 10)) .filterBounds(bounds) .select(polarization) var before = collection.filterDate(before_start, before_end) var after = collection.filterDate(after_start, after_end) print("before", before) print("after", after) // Generating Reference and Flood Multi-temporal SAR Data ------------------------ // Mean Before and Min After ------------------------ var mean_before = before.mean().clip(bounds) var min_after = after.min().clip(bounds) var max_after = after.max().clip(bounds) var mean_after = after.mean().clip(bounds) Map.addLayer(mean_before, {min: -29.264204107025904, max: -8.938093778644141, palette: []}, "mean_before",0) Map.addLayer(min_after, {min: -29.29334290990966, max: -11.928313976797138, palette: []}, "min_after",1) // Flood identification ------------------------ // NDFI ------------------------ var ndfi = mean_before.abs().subtract(min_after.abs()) .divide(mean_before.abs().add(min_after.abs())) var ndfi_filtered = ndfi.focal_mean({radius: 50, kernelType: 'circle', units: 'meters'}) // NDFI Normalization ----------------------- var ndfi_min = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.min(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_max = ndfi_filtered.reduceRegion({ reducer: ee.Reducer.max(), geometry: bounds, scale: 10, maxPixels: 1e13 }) var ndfi_rang = ee.Number(ndfi_max.get('VH')).subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_subtctMin = ndfi_filtered.subtract(ee.Number(ndfi_min.get('VH'))) var ndfi_norm = ndfi_subtctMin.divide(ndfi_rang) Map.addLayer(ndfi_norm, {min: 0.3862747346632676, max: ... Visit https://dataone.org/datasets/sha256%3A5a49b694a219afd20f5b3b730302b6d76b7acb1cc888f47d63648df8acd4d97e for complete metadata about this dataset.

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The American Indian/Alaska Native/Native Hawaiian (AIANNH) Areas file includes the following legal entities: federally recognized American Indian reservations and off-reservation trust land areas, state-recognized American Indian reservations, and Hawaiian home lands (HHLs). The statistical entities included are Alaska Native village statistical areas (ANVSAs), Oklahoma tribal statistical areas (OTSAs), tribal designated statistical areas (TDSAs), and state designated tribal statistical areas (SDTSAs). Joint use areas included in this file refer to areas that are administered jointly and/or claimed by two or more American Indian tribes. The Census Bureau designates both legal and statistical joint use areas as unique geographic entities for the purpose of presenting statistical data. Note that tribal subdivisions and Alaska Native Regional Corporations (ANRCs) are additional types of American Indian/Alaska Native areas stored by the Census Bureau, but are displayed in separate files because of how they fall within the Census Bureau's geographic hierarchy. The State of Hawaii's Office of Hawaiian Home Lands provides the legal boundaries for the HHLs. The boundaries for ANVSAs, OTSAs, and TDSAs were delineated for the 2020 Census through the Participant Statistical Areas Program (PSAP) by participants from the federally recognized tribal governments. The Bureau of Indian Affairs (BIA) within the U.S. Department of the Interior (DOI) provides the list of federally recognized tribes and only provides legal boundary information when the tribes need supporting records, if a boundary is based on treaty or another document that is historical or open to legal interpretation, or when another tribal, state, or local government challenges the depiction of a reservation or off-reservation trust land. The generalzied boundaries for federally recognized American Indian reservations and off-reservation trust lands are as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries for state-recognized American Indian reservations and for SDTSAs are based on those delineated by state governor-appointed liaisons for the 2020 Census through the State American Indian Reservation Program and PSAP respectively.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.

Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (not yet published) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.

Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.

Change Log: 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

22 Nov 2023: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.

The Digital Bedrock Geologic-GIS Map of Marsh-Billings-Rockefeller National Historical Park and the Town Boundary of Woodstock, Vermont 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 (mabi_bedrock_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 (mabi_bedrock_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 (mabi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mabi_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 (mabi_bedrock_geology_metadata_faq.pdf). Please read the mabi_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: Vermont 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 (mabi_bedrock_geology_metadata.txt or mabi_bedrock_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).

Contains data from CARTO.CTYLIMIT.Updated as needed.

Boundaries of Guayaquil neighborhoods, recreated from Google Maps, Wikimapia, and OpenStreetMap.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

The United Nations Geospatial Data, or Geodata, is a worldwide geospatial dataset of the United Nations. The United Nations Geodata is provided to facilitate the preparation of cartographic materials in the United Nations includes geometry, attributes and labels to facilitate the adequate depiction and naming of geographic features for the preparation of maps in accordance with United Nations policies and practices. The geospatial dataset include polygons/areas of countries (BNDA_simplified). Please refer this page for more information.

This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

This dataset provides the locations of oil and gas (O&G) related infrastructure globally. The Oil and Gas Infrastructure Mapping (OGIM) database is a project developed by the Environmental Defense Fund (EDF) and MethaneSAT LLC, a wholly-owned subsidiary of EDF. The primary objective of developing a standardized O&G infrastructure database such …

Tacoma 1990 - USGS 1 meter Aerials for ArcGIS Online/Bing Maps/Google Maps, etc. This layer includes UP, Fircrest, Fife, and some of Federal Way.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: U.S. Geological SurveyFlight Time: July, 1990Metadata (Internal use only)Earth Explorer Full Display of Record 1 (Internal use only)Original ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.632392 East longitude: -122.304303 North latitude: 47.380453 South latitude: 47.118196Extent in the item's coordinate system: West longitude: 1112120.835383 East longitude: 1191291.333557 South latitude: 658000.509741 North latitude: 751710.870268