Sierra Leone GIS data

uav imagery by #CODOHSAPA, acquired 2026-09-29 to 2026-09-29, 5.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM1 boundaries for Sierra Leone: 4 units, representing 2014. Source: Government of Sierra Leone, OCHA ROWCA.

geoBoundaries ADM3 boundaries for Sierra Leone: 165 units, representing 2016. Source: openAfrica, geoBoundaries. Open Data

satellite imagery by Osman Kargbo, acquired 2026-06-15 to 2026-06-15, 5.2 cm per pixel, published on OpenAerialMap.

aircraft imagery by Osman Kargbo, acquired 2026-03-31 to 2026-03-31, 21.0 cm per pixel, published on OpenAerialMap.

uav imagery by Tommy Charles, acquired 2025-08-01 to 2025-08-01, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by CODOHSAPA/FEDURP, OSM Sierra Leone, HOT, acquired 2025-04-15 to 2025-04-15, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by Freetown Drone Club, acquired 2025-04-15 to 2025-04-15, 4.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM2 boundaries for Sierra Leone: 14 units, representing 2017. Source: Government of Sierra Leone, OCHA ROWCA.

uav imagery by #CODOHSAPA, acquired 2025-04-03 to 2025-04-03, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by HOT, acquired 2025-04-16 to 2025-04-17, 3.9 cm per pixel, published on OpenAerialMap.

uav imagery by HOT, acquired 2025-04-16 to 2025-04-17, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by Tommy Charles, acquired 2025-08-01 to 2025-08-01, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by DroneTM, acquired 2025-10-22 to 2025-10-22, 3.9 cm per pixel, published on OpenAerialMap.

uav imagery by HOT, acquired 2026-02-07 to 2026-06-03, 5.0 cm per pixel, published on OpenAerialMap.

aircraft imagery by Osman Kargbo, acquired 2026-03-31 to 2026-03-31, 21.5 cm per pixel, published on OpenAerialMap.

satellite imagery by Osman Kargbo, acquired 2026-04-01 to 2026-04-01, 5.1 cm per pixel, published on OpenAerialMap.

uav imagery by Tommy Charles, acquired 2025-08-01 to 2025-08-01, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Freetown Drone Club, acquired 2025-04-19 to 2025-04-19, 4.0 cm per pixel, published on OpenAerialMap.

uav imagery by CODOHSAPA, acquired 2026-09-14 to 2026-09-14, 5.2 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM0 boundaries for Sierra Leone: 1 units, representing 2016. Source: openAfrica, geoBoundaries. Open Data

uav imagery by Osman Kargbo, acquired 2026-10-14 to 2026-10-14, 5.0 cm per pixel, published on OpenAerialMap.

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3, ADM4. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Sierra Leone. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Sierra Leone. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

**ADAM ID: **FL-20250918-SLE-02 A flood occurred on Sep 18 2025 in Sierra Leone. It affected an estimated 37351 people and 211 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

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 Sierra Leone **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0688 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0032 million km (4.6778%) - Unpaved roads: 0.0218 million km (31.6255%) About 0.0438 million km (63.6967%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0005 million km (1.1796%) 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

Sierra Leone administrative level 0-4 boundaries (COD-AB) dataset version 02. This dataset is structured into 4 levels: - Admin 1: 5 - Admin 2: 16 - Admin 3: 167 - Admin 4: 1316 Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 15 December 2023: valid for use by the humanitarian community - 16 August 2017: last edit to the dataset before publishing - 16 August 2017: boundaries created by the source Contributed by OCHA West and Central Africa (ROWCA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Sierra Leone Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015