This map of Minnesota cities, townships, and counties was published by MnGeo in January 2019. The primary data set for the map is the "Cities, Townships, and Unorganized Territories" (MnCTU) data maintained by the Minnesota Department of Transportation. Other reference data on the map include County Seats and Other Cities, County Boundaries, Interstate, US Trunk, and State Trunk Highways, Major Rivers, Lakes, County and State Boundaries. The download is a PDF file with embedded layers that can be printed at E-scale (36" x 48").

This layer is a component of Natural resources interactive map.

This feature class is basically a representation of all cities/towns and townships within Douglas County.This is the most up to date boundary for Douglas County with annexations for Alexandria City. Current boundary used in apps.

Map Catalog is powered by the Memento Server software and provides a continuous view across multiple geospatial PDFs. The PDF maps currently available are 1K USNG (topo and aerial) maps from four metro counties(Anoka, Carver, Dakota and Ramsey), 1K USNG Topo of cities and state parks in Minnesota, 10K USNG Aerial maps for Minnesota, US Topo for the metro and Dakota County Park maps, City Street maps and Half Section maps. Map update frequency varies.

Circulator routes and local transportation options within Washington County, MN.Twin Cities Metro Transit route data provided by Metropolitan Council.This map is used by the Transportation Finder application

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.

The regional bikeways dataset was created by the Minnesota Dept of Transportation (MNDOT) in 2003. It has been maintained and updated by the Land Management Information Center (LMIC) through contract with the Metropolitan Council.

Dataset includes bicycle routes within nine Twin Cities metropolitan counties: Anoka, Carver, Chisago, Dakota, Hennepin, Ramsey, Scott, Washington, and Wright. The bikeways are from a number of sources including the Metro Bicycle Network map book (2001), supplemented by information from maps published by city, state, county and regional government agencies, and city and county planning maps. The map shows on-road and off-road bikeways, proposed and existing bikeways, and includes bike lanes, bike-able road shoulders, and trails.

The majority of the bikeways follow road centerlines of the MNDOT BaseMap 2002-Roads. A description of the bikeways attributes and sources of data are included in Section 5 of this document - Entity and Attribute Overview.

The 2022 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 cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs are based on those delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

This medium-scale (nominally 1:24,000) dataset represents the boundaries of cities, townships, and unorganized territories (CTUs) in Minnesota. The Minnesota Geospatial Information Office created the initial CTU dataset by updating a municipal boundary file maintained by the Minnesota Department of Transportation (MnDOT). Update information was gathered primarily from boundary adjustment records maintained by the Office of Administrative Hearings, Municipal Boundary Adjustment Unit. MnDOT has maintained the file since 2014.

Note: Cities and Townships represented in this dataset are political (civil) townships as recognized by the State of MN, not congressional or public land survey townships. Unorganized territory subdivisions are those defined by the U.S. Bureau of the Census, which often differ from those defined by a county.

Check other metadata records in this package for more information on CTUInformation.


Link to ESRI Feature Service:

City, Township, and Unorganized Territory in Minnesota: City, Township, and Unorganized Territory

A high-resolution (1-meter) land cover classification raster dataset was completed for three different geographic areas in Minnesota: Duluth, Rochester, and the seven-county Twin Cities Metropolitan area. This classification was created using high-resolution multispectral National Agriculture Imagery Program (NAIP) leaf-on imagery (2015), spring leaf-off imagery (2011- 2014), Multispectral derived indices, LiDAR data, LiDAR derived products, and other thematic ancillary data including the updated National Wetlands Inventory, LiDAR building footprints, airport, OpenStreetMap roads and railroads centerlines. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach to classify 12 land cover classes: Deciduous Tree Canopy, Coniferous Tree Canopy, Buildings, Bare Soil, other Paved surface, Extraction, Row Crop, Grass/Shrub, Lakes, Rivers, Emergent Wetland, Forest and Shrub Wetland.

We mapped the 12 classes by using an OBIA approach through the creation of customized rule sets for each area. We used the Cognition Network Language (CNL) within the software eCognition Developer to develop the customized rule sets. The eCognition Server was used to execute a batch and parallel processing which greatly reduced the amount of time to produce the classification. The classification results were evaluated for each area using independent stratified randomly generated points. Accuracy assessment estimators included overall accuracies, producers accuracy, users accuracy, and kappa coefficient. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of land cover classes with highly accurate results.

A dataset listing Minnesota counties by population for 2024.

High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).

First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).

This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).

The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat

Sources:
Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.

Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020

Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016

NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.

Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.

OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

The goal of the Composite Image Service is to provide a single layer image service with the 'best' air photo or satellite image available for a certain extent. The images are provided as a WMS (web map service). The primary focus is the Twin Cities Metropolitan Area and the State of Minnesota. The secondary interest is the areas bordering Minnesota, especially Wisconsin counties close to the metro area. Imagery is evaluated based on criteria of color, currency, resolution, coverage, season, leaf cover and image quality in order to determine which imagery to include at which scale. See the Lineage field for a list of imagery layers that are visible at different scales. More information about this map service and the imagery resources displayed at various scales is available on the MnGeo Composite Image Service webpage: http://www.mngeo.state.mn.us/chouse/wms/composite_image.html

We collected open and publicly available data resources from the web from administrative, county- or state-level institutions in the United States and integrated and harmonized cadastral parcel data, tax assessment data, and building footprint data for 33 counties, where building footprint data and building construction year information was available. The result of this effort is a unique dataset which we call the Multi-Temporal Building Footprint Dataset for 33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building footprints including their construction year, and is available in ESRI Shapefile format (Spatial reference system: SR-ORG:7480), organized per county. We compared the MTBF-33 dataset quantitatively to other building footprint data sources, achieving an overall F-1 score of 0.93. Moreover, we compared the MTBF-33 dataset qualitatively to urban extents from historical maps and find high levels of agreement. The MTBF-33 dataset can be used to support historical building stock assessments, to derive retrospective depictions of built-up areas from 1900 to 2015, at fine spatial and temporal grain and can be used for data validation purposes, or to train statistical learning approaches aiming to extract historical information on human settlements from remote sensing data, historical maps, or similar data sources.

Data sources: Boulder County (Colorado) Open Data Catalog / Florida Geographic Data Library / Hillsborough County, Florida / City of Tampa / Manatee County, Florida / Sarasota County, Florida / City of Evansville, Vanderburgh County, Indiana / Baltimore County Government, Maryland / Bureau of Geographic Information (MassGIS), Commonwealth of Massachusetts, Executive Office of Technology and Security Services / City of Boston / MetroGIS, Minnesota Geospatial Commons, Minnesota Geospatial Information Office, Anoka County, Carver County, Dakota County, Hennepin County, Ramsey County, and Washington County, Minnesota / Monmouth County, New Jersey / City of New York / Mecklenburg County, North Carolina. Data scraping was performed in 2016.

A downloadable, printable 8.5 x 11 inch PDF map of the City of Carver and surrounding area.

Fugro Horizons Inc. acquired highly accurate Light Detection and Ranging (lidar) elevation data for the Twin Cities metropolitan region in east-central Minnesota in Spring and Fall 2011, with some reflights in Spring 2012. The data cover Anoka, Benton, Carver, Dakota, Goodhue, Hennepin, Isanti, Kanabec, Meeker, Mille Lacs, Morrison, Ramsey, Scott, Sherburne and Washington counties.

Most of the data was collected at 1.5 points/square meter. Smaller areas were collected with 2 points/square meter and with 8 points/square meter:
1. 1.5 points/square meter covers Morrison, Mille Lacs, Benton, Isanti, Sherburne, Anoka, Meeker, Hennepin, Washington, Carver, Scott, and Goodhue counties.
2. 2 points/square meter covers the Dakota Block (southern 2/3 of Dakota County)
3. 8 points/square meter covers portions of Minneapolis/St. Paul and the City of Maple Grove
See map of block boundaries: https://www.mngeo.state.mn.us/chouse/elevation/metro_data_delivery_dates.pdf

Data are in the UTM Zone 15 coordinate system, NAD83 (HARN), NAVD88 Geoid09, meters. The tiling scheme is 16th USGS 1:24,000 quadrangle tiles.

The vendor delivered the data to the Minnesota Department of Natural Resources (DNR) in several formats:

1. One-meter digital elevation model
2. Edge-of-water breaklines
3. Classified LAS formatted point cloud data

DNR staff quality-checked the data and created three additional products: two-foot contours, building outlines and hillshades.

This metadata record was created at the Minnesota Geospatial Information Office using information supplied by the vendor and by DNR.

This layer defines the district boundaries for the Metropolitan Council members.

NOTES:
- The Metropolitan Council Districts were enacted on May 19, 2023, using plan MC2023-2.
- Council member names and spellings are taken directly from the Metropolitan Council web site and press releases.
- Hanover, New Prague, Northfield, and Rockford have been removed from the district boundaries.
- Cities of Minneapolis, St. Paul, Edina, Maplewood and Woodbury have populations split into more than one Council District.
- The actual descriptions of Metropolitan Council District boundaries were taken from Minnesota Statutes 2022, 473.123 http://www.revisor.leg.state.mn.us/stats/473/123.html
- The 2023 plan files, details and components passed by the 2023 MN Legislature can be found at https://www.gis.lcc.mn.gov/redist2020/MetCouncil/mc2023_2/.
- For the map and data available from the Metropolitan Council, district boundaries were adjusted to conform to existing geography employed by the Metropolitan Council.
- Adjustments for district boundaries that fall on city/township boundaries are updated on a quarterly basis with data from counties.
- Met Council Districts are defined every 10 years.

description: This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. This is a graphic representation of the data stewards based on PLSS Townships in PLSS areas. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In other implementations this may have been termed the alternate source, meaning alternate to the BLM. But in the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward.; abstract: This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. This is a graphic representation of the data stewards based on PLSS Townships in PLSS areas. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In other implementations this may have been termed the alternate source, meaning alternate to the BLM. But in the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward.

Exterior boundary, Annexation boundary, and County boundary coverages are examined to remove all overshoots, unwanted intersections; insure polygons are closed; and to see that there are no missing or duplicate polygon labels. Each symbolic layer is individually examined for completeness. There is no line duplication in exterior or annexation coverages. MCD's with detached or non-contiguous units (polygons) have the same polygon link/label code in each of their units. Completeness in the checking process is examined through the examination and comparison of all detail collected by County Staff. Sources used included: City Clerks, County Auditor's Office, Washington County Historical Courthouse, MN State Historical Society, Secretary of State, Minnesota Municipal Board, and MNDOT. https://www.co.washington.mn.us/1609/Municipal-Boundaries

A downloadable, printable 8.5 x 11 inch PDF map of the City of Waconia and surrounding area.