The National Bridge Inventory dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 620,000 of the Nation"s bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data present a complete picture of the location, description, classification, and general condition data for each bridge. The Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation"s Bridges contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519105. For additional questions regarding regulations for the National Bridge Inventory, the Specifications of the National Bridge Inventory (SNBI) manual (https://www.fhwa.dot.gov/bridge/snbi.cfm), how an attribute is coded, please contact Wendy McAbee at wendy.mcabee@dot.gov. For questions on the geospatial compnent of the dataset, contact the NTAD team at NTAD@dot.gov. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519105

Road and Bridge Projects application, designed for the Pennsylvania Department of Transportation (PennDOT) to provide the public with an informational portal for learning about and viewing improvements to state highways and bridges.The Road and Bridge Projects application is a web-based GIS mapping application for highway and bridge projects. This application allows users to map and obtain information for highway and bridge projects, and to search these projects by criteria such as:AddressCountyStatewidePennDOT Engineering DistrictLegislative DistrictPlanning PartnerInterstate

The NBI is a collection of information (database) describing the more than 600,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal lands. It presents a State by State summary analysis of the number, location, and general condition of highway bridges within each State.

© Federal Highway Administration This layer is sourced from maps.bts.dot.gov.

The NBI (NTAD 2015) is a collection of information (database) describing the more than 610,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal lands. It presents a State by State summary analysis of the number, location, and general condition of highway bridges within each State. Please note: 11,168 records in this database were geocoded to latitude and logtitude of 0,0 due to lack of location information or errors in the reported locations.

© Federal Highway Administration

This application provides the location and condition of all bridges and large culverts in the Commonwealth. The solid icons on the map represent culverts while the open icons represent bridges. Zoom in on the map to display bridges on primary and secondary routes. Click on any icon for additional information about the bridge or culvert. Due to data collection efforts some bridges may show multiple icons – we are working to correct this issue. Bridge condition is not a measure of safety. All bridges are inspected regularly and any bridge determined to be unsafe is immediately closed until repairs can be made.

The National Bridge Inventory (NBI) is a database, compiled by the Federal Highway Administration (FHWA), with information of all the bridges and tunnels in the United States that have roads passing above or below them.This Excel Workbook contains a separate sheet for each year from 2016 to 2024 for the bridges on the National Highway System (NHS). The NHS includes the Interstate Highway System as well as other roads important to the nation's economy, defense, and mobility. It was developed by the Department of Transportation (DOT) in cooperation with the states, local officials, and metropolitan planning organizations (MPOs). TPB Staff downloads NBI data annually from the FHWA. TPB staff extracts NHS bridges in the TPB Planning Region, assigns a regional unique ID, calculates the bridge condition according to the federal rule, calculates the deck area, and summarizes the bridge condition data by jurisdiction to use as input to the federally-required Performance-Based Planning and Programming (PBPP) target setting and monitoring process.TPB staff use the geographic coordinates in the NBI file to calculate the latitude and longitude in decimal degrees. Several of the latitude and longitude coordinates were incorrectly located and have been updated by the TPB staff. Each bridge has a unique ID and can be linked to the annual files by the field PBPP ID.

Staff applied the final rule to the region’s latest bridge condition data for the TPB Planning Region. The data are reported per the required performance measures (good and poor conditions), see the rule and National Bridge Inventory Guide for more information.For more information:FHWA PBPP FHWA Transportation Performance Management TPM Frequently Asked QuestionsTPM Bridge Performance Measures

This document provides information about the highest bridges on the national highway.

In practice, roads/bridges networks between towns and cities exist and are maintained. A 100 score is earned where all the following conditions are met: 1) a road/bridge network connects most towns and cities, 2) there is an entity specialized in ensuring roads are permanently safe and functional, and 3) no accidents occurred over the last year caused by roads/bridges malfunctions (holes, construction defects, etc.). Note: any type of road/bridge can be considered to score this indicator (gravel, highway, wood, concrete, etc.). A 50 score is earned where any of the following conditions apply: 1) data proves around a third of towns and cities are not connected by a road/bridge network, 2) the entity specialized in ensuring roads are permanently safe and functional admits to having insufficient budget, or 3) at least one accident occurred over the last year caused by road/bridge malfunctions (holes, construction defects, etc.). A 0 score is earned where at least one of the following conditions apply: 1) more than a third of towns and cities are not connected by a road/bridge network or the government lacks data to determine this information, 2) there is no entity mandated to ensuring roads/bridges are permanently safe and functional, or 3) several accidents occurred over the last year caused by road/bridge malfunctions (holes, construction defects, etc.). For variable descriptions, please refer to: https://www.africaintegrityindicators.org/data. For the methodology, please refer to: https://static1.squarespace.com/static/5e971d408be44753edfb976c/t/60a55f343d36117866628867/1621450563745/AII10+-+Methodology.docx+%281%29.pdf.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4981 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0621 and 0.1696 (in million kms), corressponding to 12.4573% and 34.0499% respectively of the total road length in the dataset region. 0.2665 million km or 53.4929% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0021 million km of information (corressponding to 0.7729% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

This feature class consists of point features which represent physical structures that Interstate, Primary, Secondary and Urban roads travel under or over on all Virginia Department of Transportation maintained roadways. The Linear Referencing System is based on the Virginia Department of Transportation's source system of record for road inventory, the Highway Traffic Record and Inventory System (HTRIS). Geometry and Attribution: The dynamically generated points contained within this feature class portray the beginning of structures represented on the master route in the prime and the end of structures in the non-prime directions of travel. All point locations are positioned on Linear Referencing System geometry based on latitudinal and longitudinal coordinates initially obtained from heads-up digitizing in the Geographic Information System. Structure coordinates are currently maintained in the Roadway Network System by the Structures and Bridge Division. Features on or along the road (culverts, bridges, tunnels, parallel footbridges, etc.) and also features crossing the road (overpasses, culverts, perpendicular foot bridges, etc.) are depicted in this data. Business attributes for all features are derived from PONTIS (Bridge Management System), the structure source system of record. The feature class contains condition ratings for each structure. For bridges, there will be a deck rating, a substructure rating, and a superstructure rating. For culverts there will be a culvert rating. According to the National Bridge Inspection Standards (NBIS), condition ratings are used to describe an existing bridge or culvert compared with its condition if it were new. The ratings are based on the materials, physical condition of the deck (riding surface), the superstructure (supports immediately beneath the driving surface) and the substructures (foundation and supporting posts and piers). General condition ratings range from 0 (failed condition) to 9 (excellent).

The evaluation of the MCC Cabo Verde Roads and Bridges Activity is mainly two-fold: 1) an economic analysis (Research Area 1) to understand the costs and benefits of the MCC-funded roads, and 2) performance evaluations of the road maintenance and road usage patterns conducted to complement and enhance knowledge gained through the economic analysis (Research Areas 2 and 3). The three research areas, collectively, will inform MCC on its future project design, monitoring, and implementation of roads and bridges projects and/or other relatively large infrastructure projects.

The economic analysis consists of one research area as follows: Research Area 1 tests the economic viability of MCC-funded roads and bridges by conducting a cost-benefit analysis (CBA) to estimate the ERR and net present value (NPV) of the roads and bridges. The CBA assessing the effects of the roads will employ the Roads Economic Decision (RED) model, an analytical tool used to conduct CBA for roads. The economic analysis for the bridges will be conducted by deriving benefits from time that would otherwise have been lost due to road impassibility through extreme weather events and destroyed bridge infrastructure. PostCompact CBA re-evaluates the validity of the initial assumptions made prior to the Compact. An updated ERR of the MCC-funded roads and bridges will inform MCC on the economic viability of relatively large roads and bridges infrastructure projects.

The performance evaluations are centered around three thematic areas as below: Research Area 2 will evaluate the road maintenance regime within Cabo Verde to test the sustainability of improvement in road infrastructure. The analysis will improve MCC's assumption on post-Compact maintenance and project-life assumptions about its infrastructure investments. In addition, the evaluation will examine the effect of MCC's efforts in improving the road maintenance practices in Cabo Verde under the Roads Project. Research Area 3 is a study of road users, based on origin-destination (O-D) surveys on segments of the MCC-funded roads. The data collected from the O-D surveys will inform the RED model. Information such as the cost and duration of the trips and value of the goods being transported will be analyzed. The feasibility of surveying public transport users in parallel with the O-D surveys to get a full picture of the users and beneficiaries of the road improvements will also be considered. This research area is intended to understand qualitative information on the road users and their travel patterns.

BRIDGES_SYSTEM1_INDOT_IN is a point shapefile that contains locations of all system 1 bridges in Indiana, provided by personnel of Indiana Department of Transportation (INDOT), Business Information and Technology Systems, GIS Mapping. The data set provided by INDOT was in an ESRI shapefile format and was named 'SYS1BRIDGES. 'An update was received on June 26, 2012 as an ESRI file geodatabase point feature class named 'DOTGIS_Bridge_INDOT_IN '. BRIDGES_SYSTEM1_INDOT_IN is attributed with national bridge identification (NBI) numbers. The term 'System 1 Bridges 'refers to bridges found on System 1 roads that include: Interstates, U.S. Highways, State Routes, Ramps, Institutional Roads (roads in state university properties, state hospitals, Indiana National Guard properties), and IDNR roads (roads in IDNR-owned properties maintained by INDOT). The following is excerpted from the metadata provided by INDOT for the source shapefile SYS1BRIDGES.SHP: 'Sys1Bridges.shp is a point shapefile that shows the location of system 1 bridges. The data was generated at a scale of 1:2,000,000. '

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.8443 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0657 and 0.081 (in million kms), corressponding to 7.7851% and 9.594% respectively of the total road length in the dataset region. 0.6975 million km or 82.6209% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0123 million km of information (corressponding to 1.764% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 6.1163 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.6539 and 0.0775 (in million kms), corressponding to 10.6909% and 1.2663% respectively of the total road length in the dataset region. 5.385 million km or 88.0428% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.012 million km of information (corressponding to 0.2231% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0005 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0001 and 0.0 (in million kms), corressponding to 16.9583% and 8.6397% respectively of the total road length in the dataset region. 0.0004 million km or 74.402% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.8552% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.1279 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.025 and 0.0415 (in million kms), corressponding to 19.5376% and 32.4556% respectively of the total road length in the dataset region. 0.0614 million km or 48.0068% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0007 million km of information (corressponding to 1.1673% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4783 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0081 and 0.3254 (in million kms), corressponding to 1.6908% and 68.0296% respectively of the total road length in the dataset region. 0.1448 million km or 30.2796% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0001 million km of information (corressponding to 0.067% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Bridges include foot, bridle trail, and vehicle bridges of widely varying widths, spans, heights, and types of construction. In the interest of limiting the classifications within this compilation, the less frequent underpass and the minor culvert are embraced within this section. In outward appearance, the bridge calls most importantly for visible assurance of strength and stability. To be entirely successful, it is not enough for the bridge to be functionally adequate within the exact knowledge of the engineer; it must proclaim itself so to the inexact instincts of the layman. In gesture to the lay concept of structural sufficiency, it is pardonable park practice to venture well beyond sheer engineering perfection in the scaling of materials to stresses and strains. The attainment of "the little more" that is so desired by those who would have an eye-appeal scale brought to the slide-rule, is all too rare in park bridges. Rather is there a too prevalent flimsiness, ocular rather than structural. Considerably fewer bridges fail to satisfy by seeming too ponderous for their function. After the attainment of a sufficiency in material pleasing to the eye, the next demand to be made upon bridges would be for variety, avoiding the commonplace at one extreme, and the fantastic at the other. The ranges of use, span and height, and the broad fields of materials, arch and truss forms, local practices—among other variety-making possibilities—promise endless combinations and cross-combinations that could make for such individuality among bridges that none need ever appear the close counterpart of another. This presentation seeks merely to focus on the characteristics that bring to bridges the most promise of compatibility with natural environment. There is elsewhere abundant information, including diagrams, rules and formulae, for the design of structurally enduring bridges. Much more limited is the field of source material that concerns itself with bridges that, by reason of appropriateness to natural environment, truly deserve to endure. There are far too many bridges which, after breaking every commandment for beauty and fitness, seem to have sought to wash away all sins through the awful virtue of permanence. Such penitent bridges should have no place in our parks. The quality of permanence cannot be considered a virtue in itself. Unless every other desirable virtue, big or little, is present, permanence is only a vicious attribute. In general, bridges of stone or timber appear more indigenous to our natural parks than spans of steel or concrete, just as the reverse is probably true for bridges in urban locations or in connection with broad main highways. Probably there are few structures so discordant in a wilderness environment as bridges of exposed steel construction. Too great "slickness" of masonry or timber technique is certain to depreciate the value of these materials for park bridges. Rugged and informal simplicity in use is indisputably the specification for their proper employment in bridges. In no park structure more than bridges is it of such importance to steer clear of the common errors in masonry. Shapeless stones laid up in the manner of mosaic are abhorrent in the extreme. In bridges particularly is there merit in horizontal coursing, breaking of vertical joints, variety in size of stones—all the principles productive of sound construction and pleasing appearance in any use of masonry. The curve of the arch, the size of the pier, the height of the masonry above the crown of the arch are all of great importance to the success of the masonry bridge. Timber bridges may utilize round or squared members to agreeable results. Squared timbers gain mightily in park-like characteristic when hand-hewn. A common fault in bridges is the too abrupt termination of the parapet, railing, or wing wall. These should carry well beyond the abutments. In general disfavor for park use are bridges of the open wood truss type. There seem to be no arguments to their advantage, while many are raised against them. In spite of most careful detailing to prevent water entering and lying in the joints, this is hard to overcome entirely. Shrinking of the timbers, rack under impact and strain, and rot developing in the opening joints speed the deterioration of this type of construction. It is short-lived and soon unsafe. The culvert is too often handled as a conspicuous bridge, when in reality it is merely a retaining wall pierced by a drain. The facing of the culvert, like the treatment of almost every other facility in natural parks, should be first and always informal and inconspicuous. Facing and culvert proper should be adequate in materials and in workmanship so that once constructed both can be forgotten and make no demands upon maintenance appropriations. The culvert proper is sometimes of local stone when this is abundant and workable, but if, as is more frequently the case, it is of concrete or of galvanized iron, reasonable concealment of the fact is to be striven for. The retaining wall that is the end wall or facing of the culvert should avoid disclosing that it is a mere veneer by extending well into the culvert opening. Natural rock is certainly the preferred material for the end walls. It may be laid either in mortar, or dry, but the latter method of laying to be lasting should be undertaken only when the available stone is of suitably large size. If stone is not available locally or from within a reasonable distance, concrete or wood must be resorted to in constructing the retaining wall. Either is an unsatisfactory substitute for the stone wall—concrete because of its harsh surface, and lack of permanence if inexpertly mixed, and wood because of its tendency to deteriorate rapidly under conditions of moisture. As much care should be given to the design and execution of culvert end walls as to other park structures. Usual mistakes are insufficient care in the handling of mortar, resulting in sloppy joints, and lack of variety in stone sizes, leading to monotony and formality of surface pattern. These faults are common to much contemporary stone work, not limited to park construction only.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.7949 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.3143 and 0.1956 (in million kms), corressponding to 17.5126% and 10.8968% respectively of the total road length in the dataset region. 1.285 million km or 71.5906% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0112 million km of information (corressponding to 0.8711% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.