Global transport data

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information. This tile layer presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered in a style similar to the Esri Navigation Dark Mode map. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

This presents the Enhanced Contrast Dark Base style (World Edition) and provides the base layer for a dark-colored basemap that aims to meet the requirements of the WCAG (Web Content Accessibility Guidelines) AA standard, and US Government Section 508 compliance. This layer includes highways, major roads, minor roads, railways, water features, parks, landmarks and building footprints. It is designed to be used with the Enhanced Contrast Dark Reference layer that includes administrative boundaries and all labels, but it can also be used separately. Map detail is built using a hierarchy with a higher contrast than usual, and with color-vision-deficient-safe colors. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices. This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly. This layer is used in the Enhanced Contrast Dark Map web map included in ArcGIS Living Atlas of the World. See the Vector Basemaps group for other vector tile layers.  Customize this Style Learn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

Ensemble des adresses ponctuelles de la Ville de Rouyn-Noranda

Global road and highway network at 1:10 million scale from Natural Earth, covering major roads, highways, and expressways worldwide. Includes road type, name, and scale rank attributes for cartographic filtering. Use this layer for transport network analysis, accessibility studies, logistics planning, and cartographic base mapping at global and continental scales. No additional information provided.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information. This tile layer presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered in a style similar to the Esri Navigation map. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Bonaire, Sint Eustatius and Saba **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0 million km (nan%) - Unpaved roads: 0.0 million km (nan%) About 0.0 million km (nan%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (nan%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Data layers from 2025 to be used as part of the congestion management process (CMP). This version is public with usage limits. This hosted feature set has usage limits applied so it is only accessible through the Congestion Management Process Analysis Tool app and associated web map.

Railway and rail transport network of Guinea showing the extent and classification of rail infrastructure. Includes passenger and freight lines. Use this layer for rail transport planning, freight logistics analysis, infrastructure assessment, and multimodal transport mapping.

This layer contains information about Global Border Crossing Points used for humanitarian operations.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Taiwan (Province of China) **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2292 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0622 million km (27.1242%) - Unpaved roads: 0.0047 million km (2.0314%) About 0.1624 million km (70.8444%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0085 million km (5.2655%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['highway'] IS NOT NULL Features may have these attributes: - name - name:en - highway - surface - smoothness - width - lanes - oneway - bridge - layer - source - name:no This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source - name:no This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - name:en - amenity - building - port - operator:type - addr:full - addr:city - source - name:no This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source - name:nl This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - name:en - amenity - building - port - operator:type - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - name:en - amenity - building - port - operator:type - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL Features may have these attributes: - name - name:en - amenity - building - port - operator:type - addr:full - addr:city - source - name:sq - name:sr This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site' Features may have these attributes: - emergency:helipad - emergency - building - capacity:persons - addr:full - operator:type - addr:city - aeroway - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity = 'ferry_terminal' OR building = 'ferry_terminal' OR port IS NOT NULL Features may have these attributes: - port - building - addr:full - addr:city - operator:type - amenity - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site' Features may have these attributes: - emergency:helipad - emergency - building - capacity:persons - addr:full - operator:type - addr:city - aeroway - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: railway IN ('rail','station') Features may have these attributes: - ele - layer - railway - addr:full - operator:type - addr:city - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: railway IN ('rail','station') Features may have these attributes: - ele - layer - railway - addr:full - operator:type - addr:city - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: railway IN ('rail','station') Features may have these attributes: - ele - layer - railway - addr:full - operator:type - addr:city - source - name This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source - name:fr This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: - oneway - name - bridge - lanes - layer - highway - width - surface - source - smoothness This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Road network in Africa classified according to their accompanying road type classification system. The cost grid represents the time cost and it is assumed that the time required to travel from one cell to another in absence of main roads is 5 times longer than the time needed on the main road. Road network was derived from ArcWorld (ESRI, 1992). **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Cost grid (GRID format) iiasa

The Tertiary 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 tertiary 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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

The Total 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 total 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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

The Primary 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 primary 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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

The Highways 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 highways 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 a Creative Commons License (CC-0) 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](mailto:info.globio@pbl.nl) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the license Creative Commons Zero (CC0) **Online resources:** Global patterns of current and future road infrastructure. Meijer et al (2018) Env. Res. Letters Globio info

B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The third pillar measures the time and cost required to register new domestic and foreign firms.