Zambia GIS data

This datasets shows the Zambia Constituencies, Wards and Districts

Zambia administrative level 0-4 shapefiles, geodatabase, gazetteer and geoservices COD-EM datasets do not replace the authoritative COD-AB available [https://data.humdata.org/dataset/cod-ab-zmb](here); however COD-EM datasets may be preferred for cartographic purposes. See caveats. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

**UNOSAT code FL20230131ZMB** This map illustrates cumulative satellite-detected water using VIIRS in Zambia between 07 to 11 February 2023. Within the cloud free analysed areas of about 653,000 km² and based on Worldpop spatial demographic data and the maximum flood water extent, about 400,000 people out of 19 million are potentially exposed or living close to maximum floodwaters areas. 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 FL20230131ZMB** This map illustrates cumulative satellite-detected water using VIIRS in Southern, Lusaka, and Central Provinces, Zambia between 26 to 30 January 2023. Within the cloud free analyzed areas of about 20,000 km² and based on Worldpop spatial demographic data and the maximum flood water extent, about 80,000 people out of 800,000 are potentially exposed or living close to maximum floodwaters areas. 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 contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

**UNOSAT code: FL20200305ZMB** Status: Floods detected in Lunga district Further actions(s): monitoring over AOI completed Preliminary observations, impact and severity: AOI 1: No satellite-detected waters in Chaba city or its vicinity as of 09 March 2020 AOI 2: Mofu city appears to have no evidences of satellite-detected waters as of 09 March 2020 AOI 3: No satellite-detected waters in Samfya city as of 10 March 2020 AOI 4: Satellite-detected waters northern and eastern part of Ncheta island affecting the agricultural fields nearby as of 07 March 2020

**UNOSAT code: FL20200305ZMB** This map illustrates satellite-detected water extents in the northern part of Ncheta Island, Lunga District, Luapula Province, Republic of Zambia as observed form Pleiades imagery acquired on 7 March 2020. Within the analyzed area, a total of about 100ha of land appears to be flooded and 115 structures are completely flooded which represents about 20% of total structures in this zone. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: FL20200305ZMB** This map illustrates satellite-detected waters in Chitambo, Lunga, Samfya, Lavushimanda and Chilubi District; Central, Luapula, Muchinga and Northern Province of Zambia as observed from Sentinel-1 imagery acquired on 4 March 2020. Within the analysed area of 10,000 km2, a total of about 705 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters, about 7,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITARUNOSAT.

**UNOSAT code: FL20200305ZMB** This map illustrates satellite-detected water via VIIRS-NOAA over Central, Luapula, Muchinga and Northern Province in Republic of Zambia between the 1st and the 05th March 2020. Within the analysed extent not covered by clouds, a total of about 10,000 km2 appear to be flooded. Based on Worldpop population about 28,000 people were potentially exposed or living close to flooded areas in Lunga district. This is a preliminary analysis and has not yet been validated in the field. Please send ground fedback to UNITAR - UNOSAT.

**UNOSAT code: FL20200305ZMB** This map illustrates satellite-detected water extents in the eastern part of Ncheta Island, Lunga District, Luapula Province, Republic of Zambia as observed form Pleiades imagery acquired on 7 March 2020. Within the analyzed area several hectares of land appear to be flooded and 29 structures out of 300 are likely flooded within this area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-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 Zambia **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.3549 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.021 million km (5.9288%) - Unpaved roads: 0.0966 million km (27.2289%) About 0.2372 million km (66.8423%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0006 million km (0.2439%) 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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Zambia administrative level 0-4 boundaries (COD-AB) dataset version 02. This dataset is structured into 4 levels: - Admin 1: 10 - Admin 2: 116 - Admin 3: 156 - Admin 4: 1853 Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 29 June 2023: valid for use by the humanitarian community - 26 November 2020: last edit to the dataset before publishing - 26 November 2020: boundaries created by the source Contributed by OCHA Regional Office for Southern and Eastern Africa (ROSEA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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: amenity, building - Part of the country exports on HDX by HOT

Vector dataset representing top score, optimal/recommended locations, for Nile tilapia cage farming systems in the Republic of Zambia. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Evaluation suitability assessment. Intensive systems are characterized by high fish densities and the use of artificial feed. Recommended locations require the presence of permanent waters with a surface area larger than 5 acres. The score is determined by processing outputs from sub-models that describe biophysical, infrastructure, and demand factors: Criteria considered: a. Market accessibility, considering the weighted population and AWI in urban areas. b. Crop input the availability of agricultural by-products. c. Livestock input - based on the weighted animal density aggregation. d. Accessibility to ports - high-quality tilapia feed is often imported in large quantities. e. Potential yield of tilapia. The score is calculated as a simple arithmetic weighted sum of normalized or scaled grids, theoretically ranging from 0 to 100: (“Accessibility MajorUrbanAreas” X 0.40) + (“CropsInput” X 0.15) + (“LivestockInput” X 0.15) + (Tilapia Yield x 0.25) **Data publication:** 2024-08-06 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Nelson Ribeiro](mailto:Nelson.RosasRibeiroFilho@fao.org) **Data lineage:** Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets: a. Market Accessibility (urban areas) - OpenStreetMap and Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km. (Urban areas = pop density>1500 habitants/km² AND area>25km²) b. Water Balance: WaPOR (https://wapor.apps.fao.org/catalog/2) precipitation and evapotranspiration monthly time-series from 2009 to 2020 for calculating a mean water balance layer modelling values: (Precipitation X 1.1) - (evapotranspiration X 1.3) c. Input crops - IFPRI Global Spatially-Disaggregated Crop Production Statistics Data for 2017 (MAPSPAM) - https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a d. Input livestock – GLW4 (https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704) : weighted animal density aggregate **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

A gridded raster of population distribution for Zambia, with cell values representing population counts. The data cover the year 2030 and are published by WorldPop (University of Southampton).

Gridded population data for Zambia, published by WorldPop (University of Southampton). Each raster cell measures the spatial distribution of population, with a reference date of 2030.