Guatemala transport data

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 Guatemala **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1198 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0159 million km (13.2564%) - Unpaved roads: 0.0279 million km (23.3326%) About 0.0759 million km (63.411%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.001 million km (1.3092%) 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

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Accessibility to major cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities (>100k habitants) in the country. This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2022-11-22 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using 1. OpenStreetMap data for roads, railways, and rivers; 2. UN Map country border. 3. The HydroSHEDS 15' resolution GRID for the DEM. 4. GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to major cities (Guatemala - ~ 500 m)

Accessibility to major maritime ports is modelled as raster-based travel time/cost analysis. The following ports are included: Puerto Quetzal Puerto Barrios San Tomas de Castilla This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2022-11-22 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using 1. OpenStreetMap data for roads, railways, and rivers; 2. UN Map country border. 3. The HydroSHEDS 15' resolution GRID for the DEM. 4. GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to ports (Guatemala - ~ 500 m)

Accessibility to regional cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities surrounding the country. The following cities are included: City - Population Tuxtla Gutierrez, Mexico - 899203 Villahermosa, Mexico - 895606 San Salvador, El Salvador - 2618205 San Pedro Sula, Honduras - 2497586 Tegucigalpa, Honduras - 2940661 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2022-11-22 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using 1. OpenStreetMap data for roads, railways, and rivers; 2. UN Map country border. 3. The HydroSHEDS 15' resolution GRID for the DEM. 4. GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to regional cities (Guatemala - ~ 500 m)