Democratic Republic of Congo (DRC) Transport Infrastructures (OpenStreetMap Export)

This database shows sea and air entry points of Kinshasa city. This data was updated by Maluku commune Red Cross volunteers during the training on the usage of OSMTracker, an opensource mobile phone GPS application for the CP3/IFRC program.

Tout le réseau des 44 routes nationales de la République démocratique du Congo, extrait de la base de données OpenStreetMap. Les données sont disponibles dans un jeu de données reprenant les 44 relations (chaque route nationale correspondant à un objet), ou reprenant chacun des segments de chacune des routes nationales avec ses propriétés spécifiques.

The aim of this work is to generate two friction surfaces for modeling physical accessibility in the Democratic Republic of the Congo: one incorporating walking speed only, and another combining walking and motorized travel speeds. Motorized speeds are applied exclusively on roads that are present and passable by vehicles; in all other areas, walking speeds are used. The GRID3 COD - Travel Time Friction Surface v1.0 dataset is a spatial data layer in tiff format. The datasets contain 2 layers: (1) GRID3_COD_walk_travel_time_friction_surface_v1_0.tif - Applies walking speeds across the entire surface, adjusted for slope and elevation. (2) GRID3_COD_mix_travel_time_friction_surface_v1_0.tif - Applies motorized speeds on roads and walking speeds in all other areas. The resolution is 30 m. The unit of measurement in both surfaces is minutes per meter. Suggested Dataset Citation Center for Integrated Earth System Information (CIESIN), Columbia University. 2026. GRID3 COD - Travel Time Friction Surface v1.0. New York: Columbia University. URL. Accessed DAY-MONTH-YEAR.

République démocratique du Congo (RDC), Réseau routier local Lines + Polygons : Local roads Interactive Online Map OSM Roads Columns: osm_id, highway, name, layer, bridge, tunnel, oneway, ref , route, junction, lanes, width, maxspeed, surface, practicability, smoothness, condition, geometry OSM Filter highway in ('residential', 'living_street', 'path', 'footway', 'pedestrian', 'steps', 'cycleway', 'track', 'service', 'bridleway', 'construction', 'road') * highway=road attribute for roads not yet classified. The Major Roads dataset is available from the Major Roads network dataset

République démocratique du Congo (RDC), Réseau routier principal Lines + Polygons : Main roads Network Interactive Online Map OSM Roads Columns: osm_id, highway, name, layer, bridge, tunnel, oneway, ref , route, junction, lanes, width, maxspeed, surface, practicability, smoothness, condition, geometry OSM Filter WHERE highway in ('motorway','motorway_link','trunk','trunk_link','primary','primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') OR route='ferry' OR amenity in ('ferry_terminal') OR ( (bridge is not null OR tunnel is not null ) AND highway in ('motorway','motorway_link','trunk','trunk_link','primary', 'primary_link','secondary','secondary_link','tertiary','tertiary_link', 'unclassified') ) The second part of the Road network is available from the Local Roads dataset

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 Democratic Republic of the Congo **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.6182 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0114 million km (1.8449%) - Unpaved roads: 0.2246 million km (36.3305%) About 0.3822 million km (61.8246%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0011 million km (0.2792%) 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