Libyan Arab Jamahiriya GIS data

satellite imagery by aaaa, acquired 2026-03-03 to 2026-03-04, 3088.8 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM1 boundaries for Libya: 22 units, representing 2021. Source: Natural Earth.

geoBoundaries ADM0 boundaries for Libya: 1 units, representing 2021. Source: Natural Earth.

Location of wastewater treatment plants in East Libya

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.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

This dataset contains the following administrative boundaries: ADM0, ADM1. 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 Libya. 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 Libya. 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

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 Libya **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2478 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0573 million km (23.1418%) - Unpaved roads: 0.057 million km (22.9852%) About 0.1335 million km (53.873%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.0209%) 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

Libya administrative level 0-2 boundaries (COD-AB) dataset version 01. This dataset is structured into 2 levels: - Admin 1: 3 province - Admin 2: 22 governorate (muhafazat) Dates associated with this dataset: - 19 December 2024: dataset reviewed for accuracy and completeness - 07 May 2018: valid for use by the humanitarian community - 13 November 2018: last edit to the dataset before publishing - 13 November 2018: boundaries created by the source Contributed by OCHA Middle East and North Africa (ROMENA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

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

# Libya Floods: Damage and Flood Proxy Maps, Sep 2023 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage and flood proxy maps for Libya Floods (Sep 2023) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Libya, Floods, 12 Sep 2023, v0.9** - **EOS-RS Damage Proxy Map: Libya, Floods, 12 Sep 2023, v0.9** - **EOS-RS Flood Proxy Map: Libya, Floods, 12 Sep 2023, v0.5** **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2023), Validated with FirstLook Imagery © 2023 Maxar

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

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

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

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. 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, shop, tourism, man_made - Part of the country exports on HDX by HOT

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

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. 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 - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental 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: waterway, water, natural - 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

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban 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: building - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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, healthcare - 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

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. 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: place, landuse - Part of the country exports on HDX by HOT

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

This layer shows the high-voltage power lines identified and extracted from the OpenStreetMap (OSM) collaborative database. The data reflects the state of the database at the time of extraction. **Data publication:** 2026-09-11 **Contact points:** Metadata Contact: [Ioannis Vaskalis](mailto:Ioannis.Vaskalis@fao.org) **Data lineage:** More information on the data and its methodology can be found here. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

This dataset provides global sub-basin boundaries at Pfafstetter Level 6, derived from the HydroSHEDS (Hydrological data and maps based on Shuttle Elevation Derivatives at Multiple Scales) database. **Data creation:** 2026-09-10 **Contact points:** Resource Contact: [Fabien Lezeau](mailto:fabien.lezeau@fao.org) **Data lineage:** Lehner, B., Verdin, K., Jarvis, A. (2008). New global hydrography derived from spaceborne elevation data. Eos, Transactions, American Geophysical Union, 89(10): 93–94. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0). **Online resources:** waterbaselinedata_watershed

Extent and names of the transboundary aquifers in Libya. **Data creation:** 2026-09-10 **Contact points:** Resource Contact: [Fabien Lezeau](mailto:fabien.lezeau@fao.org) **Data lineage:** GRAC and UNESCO-IHP (2025) Transboundary Aquifers of the World Map. Edition 2025. **Resource constraints:** The map and its attribute table are released under the Creative Commons Attribution-ShareAlike 3.0 Intergovernmental Organization license (CC BY-SA 3.0 IGO). **Online resources:** waterbaselinedata_aquifers

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Railway and rail transport network of Libya showing the extent and classification of rail infrastructure. Includes passenger and freight lines. Use this layer for rail transport planning, freight logistics analysis, infrastructure assessment, and multimodal transport mapping.

Road and transport network of Libya showing the classification and extent of road infrastructure. Includes primary roads, secondary roads, tracks and footpaths where available. Use this layer for route planning, accessibility analysis, transport modelling, and logistics planning.

A 1-kilometre raster of population for Libya, published by Orion Terrametrics. Each cell value represents the number of people.

Spatial distribution of estimated births across Libya at 1km resolution. Each grid cell shows the estimated number of live births. Use this layer for maternal health planning, antenatal care facility siting, health resource allocation, and reproductive health programme design.

Point locations of health facilities in Libya including hospitals, health centres, clinics, dispensaries and health posts. Each feature records the facility name, type and ownership. Use this layer to map healthcare coverage, identify service gaps, plan facility distribution, and support health sector analysis.