Dominica GIS data

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.

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 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 Dominica **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0017 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0004 million km (23.2719%) - Unpaved roads: 0.0001 million km (7.7348%) About 0.0011 million km (68.9933%) 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 (0.6693%) 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

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

Sea ports and ferry terminals from OSM, tagged via `amenity`, `building`, or `port`. Useful for maritime logistics, supply-chain analysis, and coastal access planning. OpenStreetMap is a free, collaborative world map built by volunteers. Completeness varies by region: urban areas are usually well-mapped, remote areas may be incomplete. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap - OSM tags: Key:amenity, Key:building, Key:port - Part of many exports grid on HDX by the 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

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

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

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

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

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

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

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

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).

Polygon features representing landslide boundaries in Dominica, published by Orion Terrametrics. Each feature records a landslide type, code, area and date, with imagery from Pléiades.

Polygon features representing landslide parts in Dominica, published by Orion Terrametrics. Each feature records a part type, landslide type, code, area and date, with imagery from Pléiades.

Shapefile containing numbers of affected people and households in Dominica. The data also shows number of damaged buildings (destroyed, major and minor) as collated by the Dominica Red Cross as of 17 September 2015.

Dominica administrative level 0-1 boundaries (COD-AB) dataset. The date that these administrative boundaries were established is unknown. This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months. Sourced from (Source to be clarified.) Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Dominica COD-PS. As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required. Please see the COD Portal. Administrative level 1 contains 10 feature(s). The normal administrative level 1 feature type is ""currently not known"". Recommended cartographic projection: North America Albers Equal Area Conic This metadata was last updated on January 9, 2025.

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.

This map illustrates potentially damaged structures and buildings in Portsmouth  (Saint John Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 20 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area (Portsmouth and surroundings) 782 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents 55% of the total number of structures within the cloud free analysed area. The analysis could have been underestimated due to cloud cover. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in the southeastern part of Dominica (St. Patrick & St. David Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. The UNITAR-UNOSAT analysis combined with Copernicus analysis, identified 1304 potentially damaged structures within the extent of this map. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 56 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in South Roseau (Saint Georges Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 21 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area Roseau South (Castle Comfort, Citronnier and Loubiere) 863 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analysed area. Evidences of floods and mudflow could be also observed in the area of Wallhouse. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in the Central and Southern parts of Dominica (St. Patrick, St. Mak, St. George, St. Luke, St. David & St. Paul Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. The UNITAR-UNOSAT analysis combined with Copernicus analysis, identified 12,873 potentially damaged structures in this zone. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analyzed areas. Please note that some areas could not be analyzed due to the cloud cover. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in Southern Dominica as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free areas of southern Dominica 2482 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 80 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in the north-western part of Dominica (St. Andrew, St. Joseph & St. David Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 and a WorldView-3 image acquired on 1 October 2017 as post imagery. Within the extent of the analyzed areas, UNITAR-UNOSAT identified in the cloud free zones 5,609 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 90 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in Marigot(Saint Andrew Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area Marigot (Saint Andrew Parish) 1,345 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 83 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in the Southern part of Dominica (St. Luke, St. Mark, St. Georges & St. Patrick Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades images acquired on 20 and 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free zones about 5032 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 75 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potentially damaged structures and buildings in the north-western part of Dominica (St. John, St. Andrew, St. Peter & St. Joseph Parishes) as detected by satellite images acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades images acquired on 20 and 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free zones about 3723 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

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OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten', 'school', 'college', 'university','language_school') OR office='educational_institution' Features may have these attributes: name, amenity, isced:level, capacity Geometry types: point, polygon This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

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 theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) : tags['railway'] IN ('rail','station') Features may have these attributes: - name - name:en - railway - ele - operator:type - layer - addr:full - addr:city - source This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.