The TIGER/Line Shapefiles are the fully-supported, core geographic products from the US Census Bureau. They are extracts of selected geographic and cartographic information from the US Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. The all roads dataset contains all linear street features with "S" (Street) type MTFCCs in the MAF/TIGER database. These include primary roads, secondary roads, local neighborhood roads, rural roads, city streets, vehicular trails (4WD), ramps, service drives, walkways, stairways, alleys, and private roads. The all roads dataset is published at two granularities: A single table for roads in all states and territories. A separate table for each state and territory. The tables follow the naming convention all_roads_[State FIPS Code]. State FIPS codes, and the corresponding state name, are available using the State FIPS code table in BigQuery and in the sample section below. For more details on each dataset, see the TIGER/Line technical documentation published by the Census Bureau. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details. If you use any of these datasets for research purposes you should use the following citation in any resulting publications:

@phdthesis{MnihThesis, author = {Volodymyr Mnih}, title = {Machine Learning for Aerial Image Labeling}, school = {University of Toronto}, year = {2013} }

This historic dataset represents road centerlines for all public roads within the state of Minnesota as of 2012. The roads are broken from intersection to intersection and attributed with information based on their designated route. Key attribute fields include route system (Interstate, US Highway, Minnesota Highway, County State Aid Highway, County Road, Township Road, etc.), Route Number (35W, 10, 53), and Name. A detailed description of the Roads layer attributes is included in Section 5 of this document - Entity and Attribute Overview.

Some route numbers are temporary. '900' Routes are for route segments that formerly were part of a trunk highway which was turned back to a local entity. These are temporary numbers assigned while MnDOT waits for an official local designation. These numbers are assigned in the 900-999 range and are not official route numbers but just for temporarily assigning data to unnumbered routes.

***The route IDs and measures contained in this data set are part of the old TIS system and are no longer supported at MnDOT. To download the current data set containing MnDOT's new LRS route IDs and more accurate measures, go to: https://gisdata.mn.gov/dataset/trans-roads-centerlines

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. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads.

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. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

The Local Roads Density raster layer is part of the Global Roads Inventory Project (GRIP) dataset, developed to provide a more recent and consistent global roads dataset for use in global environmental and biodiversity assessment models like GLOBIO.

Data publication: 2018-01-01

Supplemental Information:

The original GRIP dataset consists of global and regional vector datasets in ESRI geodatabase and shapefile format. It is also available as global raster datasets of road density at a 5 arcminutes resolution (~8x8km) from which the local roads component has been extracted.

GRIP version 4 (GRIP4) is based on many different sources, including OpenStreetMap. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under an Open Data Commons Open Database License (ODbL) and is free to use.

Citation:

Meijer, J.R., Huijbregts, M.A.J., Schotten, C.G.J. and Schipper, A.M. (2018): Global patterns of current and future road infrastructure. Environmental Research Letters, 13-064006. Data is available at www.globio.info

Contact points:

Metadata Contact: GLOBIO

Resource constraints:

Open Data Commons Open Database License (ODbL)

Online resources:

Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters

Globio info

This road network dataset was created from data extracted from OpenStreetMap (OSM) across the geographic area of Australia on 05 August 2020. Its purpose is to represent motor-vehicle traversable public roads within Australia. Note, however, as the original dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. This road network has been topologically corrected for the purposes of network analysis for motor vehicles. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 05 August 2020. AURIN has filtered the original data and omitted features to present the topologically correct, motor-vehicle traversable road network.

PNG roads: these spatial datasets provide the delimitation of primary, secondary and tertiary roads and tracks in Papua New Guinea. The OSM dataset includes attribute information includes OSM id and road names in English where known and comprehensive track network for the mainland. This dataset can be complemented by the National Mapping Bureau (NMB) (2000) dataset. The NMB dataset includes comprehensive road network in both mainland and non-mainland districts and road surface attributes. Source: Open Street Map; Papua New Guinea National Mapping Bureau. Contributor: OCHA ROAP. Date of Dataset: Feb 24, 2015

The dataset contains roads data for the Regional Municipality of Niagara. A geocoding index file is associated with this feature class and may be used to locate intersections and or road range addressing. Attribute and geometric data is being updated with input from local municipalities and the Region of Niagara. The dataset extent corresponds to the Niagara Region and is the property of the Niagara Region.

This dataset comprises road centerlines for all roads in San Diego County. Road centerline information is collected from recorded documents (subdivision and parcel maps) and information provided by local jurisidictions (Cities in San Diego County, County of San Diego). Road names and address ranges are as designated by the official address coordinator for each jurisidcition. Jurisdictional information is created from spatial overlays with other data layers (e.g. Jurisdiction, Census Tract).The layer contains both public and private roads. Not all roads are shown on official, recorded documents. Centerlines may be included for dedicated public roads even if they have not been constructed. Public road names are the official names as maintained by the addressing authority for the jurisdiction in which the road is located. Official road names may not match the common or local name used to identify the road (e.g. State Route 94 is the official name of certain road segments commonly referred to as Campo Road).Private roads are either named or unnamed. Named private roads are as shown on official recorded documents or as directed by the addressing authority for the jurisdiction in which the road is located. Unnamed private roads are included where requested by the local jurisidiction or by SanGIS JPA members (primarily emergency response dispatch agencies). Roads are comprised of road segments that are individually identified by a unique, and persistent, ID (ROADSEGID). Roads segments are terminated where they intersect with each other, at jurisdictional boundaries (i.e. city limits), certain census tract and law beat boundaries, at locations where road names change, and at other locations as required by SanGIS JPA members. Each road segment terminates at an intersection point that can be found in the ROADS_INTERSECTION layer.Road centerlines do not necessarily follow the centerline of dedicated rights-of-way (ROW). Centerlines are adjusted as needed to fit the actual, constructed roadway. However, many road centerline segments are created intially based on record documents prior to construction and may not have been updated to meet as-built locations. Please notify SanGIS if the actual location differs from that shown. See the SanGIS website for contact information and reporting problems (http://www.sangis.org/contact/problem.html).Note, the road speeds in this layer are based on road segment class and were published as part of an agreement between San Diego Fire-Rescue, the San Diego County Sheriff's Department, and SanGIS. The average speed is based on heavy fire vehicles and may not represent the posted speed limit.

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. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

The Road Inventory is a GIS-based asset management system for the state's highway transportation system. As such, its strengths are in describing the configuration and condition of public roads and rights-of-way. It is not designed to support route-finding (e.g., shortest path applications), nor is it designed to support geocoding (although in theory intersection-based geocoding could be set up on it). It is part of the official documentation of the state road system and is used to prepare the yearly Highway Performance Monitoring System (HPMS) report to the Federal Highway Administration (FHWA). It is a record of centerline and lane miles, which are the basis of state reimbursements to localities for road maintenance expenses (Chapter 90 funds).The Massachusetts Department of Transportation Highway Division Road Inventory contains the spatial linework for all the public and a good portion of the private roadways in Massachusetts, along with roadway attributes covering the roadway classification, ownership, physical conditions, traffic volumes, pavement conditions, highway performance monitoring information, and more. This version has been processed to eliminate overlaps among features in the original distributed by MassDOT and to add pavement data, which is no longer attached by MassDOT.

OS Open Roads Shapefile containing links pertaining to the Major Road Network, as created by the Department for Transport in 2018.

See this dataset in an Interactive WebMap

If you are looking for the Strategic Road Network, please find this as part of the original, freely available, OS OpenRoads Product

Medium article https://towardsdatascience.com/road-detection-using-segmentation-models-and-albumentations-libraries-on-keras-d5434eaf73a8 Baseline

Github code: https://github.com/Diyago/ML-DL-scripts/tree/master/DEEP%20LEARNING/segmentation/Segmentation%20pipeline

DescriptionPolyline (linear) geographic features representing public roads under local jurisdiction that are functionally classified as arterials or collectors.

    Last Update
    2022


    Update FrequencyAs needed


    Data Owner
    Division of Transportation Development


    Data Contact
    GIS Support Unit


    Collection Method



    Projection
    NAD83 / UTM zone 13N


    Coverage Area
    Statewide


    Temporal



    Disclaimer/Limitations
    There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

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. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

The Secondary Roads Density raster layer is part of the Global Roads Inventory Project (GRIP) dataset, developed to provide a more recent and consistent global roads dataset for use in global environmental and biodiversity assessment models like GLOBIO.

Data publication: 2018-01-01

Supplemental Information:

The original GRIP dataset consists of global and regional vector datasets in ESRI geodatabase and shapefile format. It is also available as global raster datasets of road density at a 5 arcminutes resolution (~8x8km) from which the secondary roads component has been extracted.

GRIP version 4 (GRIP4) is based on many different sources, including OpenStreetMap. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under an Open Data Commons Open Database License (ODbL) and is free to use.

Citation:

Citations and acknowledgements of the GRIP dataset should be made as follows: Meijer, J.R., Huijbregts, M.A.J., Schotten, C.G.J. and Schipper, A.M. (2018): Global patterns of current and future road infrastructure. Environmental Research Letters, 13-064006. Data is available at www.globio.info

Contact points:

Metadata Contact: GLOBIO

Resource constraints:

Open Data Commons Open Database License (ODbL)

Online resources:

Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters

Globio info

This dataset is an extraction of roads from OpenStreetMap data made by WFP following UNSDI-T standards. The data is updated in near-real time from OSM servers and include all latest updates. NOTE: this dataset doesn't include streets and pathways that have been published on a separate dataset (streets and pathways).

More documentation on the whole process for extracting OpenStreetMap roads can be found here: https://geonode.wfp.org/documents/6823/download