Democratic Republic of the Congo GIS data

**UNOSAT code: VO20210523COD** Cette carte illustre les structures et bâtiments potentiellement impactées par la coulée de lave dans le district de Nyiragongo, province du Nord-Kivu en République Démocratique du Congo suite à l'éruption du volcan Nyiragongo du 22 mai 2021. A partir de l'analyse d'une image radar TerraSAR-X acquise le 24 mai 2021 à 16:13 UTC, UNITAR-UNOSAT a observé une coulée de lave qui s'est arrêtée à 700m des installations aéroportuaires. Sur l'étendue de l'analyse, environ 1,600 structures sont potentiellement impactées par la coulée de lave à la date d'acquisition de l'image radar. Ceci est une analyse préliminaire et n'a pas encore été validée sur le terrain. Veuillez d'envoyer vos commentaires à UNITAR-UNOSAT. Note: L'analyse de coulée de lave à partir d'une image radar peut surestimer la trace de la coulée de lave à cause du coefficient de rétrodiffusion du signal radar.

**UNOSAT code: FL20211008COD** This map illustrates satellite-detected surface waters in Ituri Province as observed from a Sentinel-2 image acquired on 04 October 2021. Within 5 km around Lake Albert, about 40 km2 of lands appear to be flooded. The water extent appears to have increased since August 2019 and based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the Zone de Santé of Nyarambe with ~5,200 people, Gethy with ~ 3,300 people and Tchomia with ~1,550 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT)

**UNOSAT code: FL20250408COD** This application provides flood impact assessment in Kinshasa, Democratic Republic of the Congo The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.

**UNOSAT code: VO20210523COD** This map illustrates potentially affected structures and buildings north of Goma airport, Nord-Kivu province, D.R. of the Congo as detected by satellite image acquired after the eruption of the Mount Nyiragongo euption of 22 May 2021. UNITAR-UNOSAT analysis used a Sentinel-1 image acquired on 25 May 2021, at 16:21 UTC, to extract the lava flow extent. Within this map extent, 1,540 structures and 9 km of roads appear to be affected by the lava. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

On 1 August 2018, the Ministry of Health of the Democratic Republic of the Congo declared a new outbreak of Ebola virus disease in North Kivu Province. The Ministry of Health, WHO and partners are responding to this event, and working to establish the full extent of this outbreak. Numbers may fluctuate on a daily basis due to many factors, including continuing monitoring, investigation and reclassification of cases. Alert and suspected cases (not reported here), are systematically investigated to confirm or exclude Ebola virus disease before inclusion in the case counts or discarded as non-cases. WHO GIS team is supporting the National Health Information System Division (DSNIS) to update the health related geographic data (health zones, areas and health facility structures) and other spatial information such as road, building, school together with the OpenStreetMap community and various partners. To carry out this work, the methodology was decided with local cartographers and together we were able to improve the data locally.

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

**UNOSAT code: VO20210523COD** This map illustrates potentially affected structures and roads in Kanyanza village, Nyiragongo district, Nord-Kivu province, D. R. of the Congo as detected by satellite image acquired after the volcanic eruption of the 22 May 2021. UNITAR-UNOSAT analysis used a Sentinel-1 image acquired on 25 May 2021, at 16:21 UTC. Within the map extent, about 144 structures and 1,400m of roads are potentially affected by the lava. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Statistiques des populations : population désagrégée par âge et sexe pour toutes les zones de santé du pays.

**UNOSAT code: FL20211008COD** This map illustrates satellite-detected surface waters in Gengere Pamoth village, Ituri province as observed from a Sentrinel-2 acquired on 09 October 2021. Within the analyzed area, the surface waters appears to have progressed inland since 27 January 2021. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

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

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**UNOSAT code: VO20210523COD** This map illustrates potentially affected structures and buildings in Nyiragongo district, Nord-Kivu province, DR Congo as detected by satellite image acquired after the eruption of Mount Nyiragongo volcano on 22 May 2021. UNITAR-UNOSAT analysis used a TerraSAR-X image acquired on 24 May 2021, at 16:13 UTC, to observe the lava flow. Within the analysis extent, about 1,600 structures are potentially affected by the lava flow. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Lava flow analysis from radar images may overestimate the presence of the lava tract due to the backscattering properties of the radar signal.

**UNOSAT code: VO20210523COD** This map illustrates the lava flow extent and the related geological information north of Goma airport, Nord-Kivu province, D.R. of the Congo as detected by satellite images acquired after the eruption of the Mount Nyiragongo on 22 May 2021. UNITAR-UNOSAT analysis used Sentinel-2 and SAOCOM-1A images acquired on 27 May 2021, at respectively 08:30 UTC and 02:21 UTC, to extract the lava flow extent. Within this map, three vents have been identified along the hillside south of Nyiragongo caldera. In addition, about 10 km of fissures have been mapped and are mainly in NNE-SSW and NNW-SSE directions. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

The Democratic Republic of the Congo Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Artisanal mining site visits in Eastern DRC

The Democratic Republic of the Congo Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

The Democratic Republic of the Congo Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

The Soil and Terrrain database of Central Africa (SOTERCAF, version 1.0) was compiled at scale 1:2 million for the Democratic Republic of Congo and at scale 1:1 million for Rwanda and Burundi. The SOTERCAF compilation has been a joint collaboration of the Soil Science Laboratory of the University of Ghent, Belgium and ISRIC - World Soil Information, Wageningen under contract with the Food and Agriculture Organisation of the United Nations. Further assistance is provided by the Department BIOT of the Hogeschool Gent, the Royal Museum for Central Africa (Tervuren) and data holders in the Democratic Republic Congo, Burundi and Rwanda. The project started in September 2005 by deriving physiographic units from SRTM grid data based on SOTER landform definitions. The database was completed in July 2006 after combining the physiographic layer with the lithology and soils layer. The border harmonization with the SOTERSAF database was finalized November 2006. SOTERCAF forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

Gridded raster of population distribution for the Democratic Republic of the Congo, published by WorldPop (University of Southampton). Each cell value measures the population count within that grid cell. The data cover the Democratic Republic of the Congo for the year 2030.

Gridded raster of population distribution for the Democratic Republic of the Congo, with cell values measuring population. Published by WorldPop (University of Southampton). The data cover the Democratic Republic of the Congo for the year 2030.

This harmonized set of soil parameter estimates for Central Africa, comprising Burundi, the Democratic Republic of the Congo and Rwanda, was derived from the Soil and Terrain Database for Central Africa (SOTERCAF ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of Central Africa, comprising some 2.4 million km2, has been characterized using 244 unique SOTER units, corresponding with 504 polygons. Each SOTER unit may consist of up to 6 soil components; each of these has been characterized by a representative profile. The main soil units mapped for the region have been characterized using 167 real profiles, selected by soil experts as being regionally representative for these units. The associated soil analytical data have been derived from soil survey reports. Gaps in the measured soil data have been filled using a scheme of using a step-wise procedure that uses taxotransfer rules. These rules were developed using some 5672 soil profiles, held in the ISRIC-WISE database, having similar FAO soil unit names as those reported in the primary SOTERCAF database. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, C/N ratio, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and available water capacity (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure. Taxotransfer rules have been flagged to provide an indication of the possible confidence in the derived data.