Haiti GIS data

Sanitation zoning assessment data for the Cap Haitien, Haiti region. Attribute table includes data on population density, socioeconomic status, suitability of pit latrines, and suggested sewage construction priority zones. These data were developed under the USAID Water and Sanitation Project in collaboration with the Cap-Haitian municipal government and DINEPA. These data do not reflect the opinion of USAID or the US Government.

Haiti administrative level 0-3 edge-matched gazetteer, shapefiles, geodatabase, and geoservice. COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. These layers are suitable for database or GIS linkage to the Haiti - Subnational Population Statistics tables using the ADM0, ADM1, ADM2, and ADM3_PCODE fields. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This dataset contains the locations of the Helicopter landing sites currently served by UNHAS in Haiti.

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.

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

**UNOSAT code: EQ20210814HTI** This map illustrates potentially damaged structures/buildings in Port-A-Piment and Coteaux Communes, South department of Haiti as detected by Pleiades image acquired on 23 August 2021. Within the analyzed area, UNOSAT has identified 41 damaged structures, 72 potentially damaged structures, 5 road obstacles, and 47 landslide locations. 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: EQ20210814HTI** This map illustrates potentially damaged structures/buildings in Les Anglais Commune, South department of Haiti as detected by Pleiades image acquired on 23 August 2021. Within the analyzed area, UNOSAT has identified 3 damaged structures, 41 potentially damaged structures, 4 road obstacles and 8 landslide locations. 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 map illustrates potentially damaged satellite-detected areas, related damage density and the identified road obstacles in the southwestern part of Haiti in Grande Anse and Sud departments, Haiti. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired on the 12 October 2016. The UNITAR-UNOSAT analysis combined with Copernicus analysis, identified 9,173 potentially damaged structures within this map extent. Additionally, 123 road obstacles and 255 population gathering sites within this area were identified in the analysis. Please note that some areas were cloud covered and could not be analysed. 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 satellite-detected areas and related damage density in the southwestern part of Haiti in Sud Department, Haiti. The UNITAR-UNOSAT analysis used WorldView-2 satellite imagery acquired on the 9 and 17 October 2016. The UNITAR-UNOSAT analysis, identified 3,678 potentially damaged structures within this map extent. Additionally, 87 road obstacles and 131 population gathering sites within this area were identified in the analysis. Please note that some areas were cloud covered and could not be analysed. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This report describes preliminary building damage analysis carried out by UNITAR-UNOSAT covering Area 1 (Jeremie and Roseaux Commune), Area 2 (Abricot, Dame-Marie, Anse d'Hainaults and Les Irois Communes), Area 3 (Corail, Pestel, Beaumont and Roseaux Communes), and Area 4 (Tiburon Commune) for a total area of approximately 1,200 Km2. Building damage analysis, including a rapid assessment of transportation network conditions and locations of spontaneous people gathering sites, was conducted by comparing the post-disaster satellite images (Pleiades acquired on 7/10/2016 for AOI1, Pleiades acquired on 12/10/2016 for AOI2, Pleiades acquired on 09/11/2016 for AOI3 and Worldview-2 acquired on 9/10/2016 & 17/10/2016 for AOI4) with available pre-disaster images (WorldView-1 on 08/12/2014, 01/05/2015, 09/05/2015 and 16/06/2015; Worldview-2 on 17/07/2016, 28/11/2014 and 14/06/2015 and Worldview-3 on 17/10/2015). UNOSAT's preliminary analysis shows a total of 40,696 buildings/structures with visible damages and approximately 508 locations with visible road obstacles and/or access constraints. In addition, 1,497 temporary people gathering sites have been identified within the analysed areas (Area 1, Area 2, Area 3 and Area 4).

This report highlights the preliminary building damage analysis including a rapid assessment of transportation network conditions and locations of spontaneous people gathering sites covering Jérémie Commune and surrounding areas.The analysis was conducted by comparing the post-disaster satellite images (Pleiades acquired on 7/10/2016) with available pre-disaster images (WorldView-1 on 01/05/2015 and 08/12/2014).

This map illustrates the satellite-detected water extent in Les Cayes and Cavaillon communes in Sud Department (Haiti) located in the southwestern part of Haiti. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 26 April 2017 and a Radarsat-2 image acquired on the 17 May 2016 and could observe areas with standing waters mainly affecting agricultural fields. The UNITAR-UNOSAT analysis identified 718 ha of flooded areas, which cover ~4% of this map's extent of ~20300 ha of land. The increase of waters within the analyzed zone is about 460%. ~155 ha of surface waters were observed the 17 May 2016 and reached ~873 ha the 26 April 2017. About 9km of roads, mainly local roads are potentially affected. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates the satellite-detected water extent in Cavaillon commune in Sud Department (Haiti) located in the southwestern part of Haiti. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 26 April 2017 and a Radarsat-2 image acquired on the 17 May 2016 and could observe areas with standing waters mainly affecting agricultural fields. The UNITAR-UNOSAT analysis identified 566 ha of flooded areas, which cover 7% of this map's extent of ~8500 ha of land. The increase of waters within the analyzed zone is about 550%. ~100 ha of surface waters were observed the 17 May 2016 and reached ~666 ha the 26 April 2017. About 6km of roads, mainly local roads are potentially affected. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.

This map illustrates the satellite-detected water and/or saturated wet soil extent in Solon Communal Section, St. Louis du Sud Commune in Sud Department located in the southwestern part of Haiti. The UNITAR-UNOSAT analysis used a WorldView-2 satellite image acquired on the 28 April 2017 and could observe areas with standing waters and saturated wet soil mainly affecting agricultural fields. The UNITAR-UNOSAT analysis identified 118 ha of those areas, which cover 8.50% of this map's extent of 1385 ha of land. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates satellite-detected potentially damaged buildings in Tiburon town, Sud department, Haiti. The UNITAR-UNOSAT analysis used a WorldView-2 satellite image acquired on the 17 July 2016 as a pre-image and a Worldview- 2 satellite image acquired on the 17 October 2016 as a post-image. The UNITAR-UNOSAT analysis identified 648 potentially damaged structures within the map extent of which 611 were identified inside the town of Tiburon. The depiction and use of town boundaries acquired from Wikimapia, are not warranted to be error- free nor do they imply official endorsement or acceptance by the United Nations. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Population Exposure Data Haiti

This report describes preliminary building damage analysis carried out by UNITAR-UNOSAT covering Area 1 (Jeremie and Roseaux Commune) and Area 2 (Abricot, Dame-Marie, Anse d’Hainaults and Les Irois Communes) for a total area of approximately 650 Km2. Building damage analysis, including a rapid assessment of transportation network conditions and locations of spontaneous people gathering sites, was conducted by comparing the post-disaster satellite images (Pleiades acquired on 7/10/2016 for AOI1 and 12/10/2016 for AOI2) with available pre-disaster images (WorldView-1 on 01/05/2015, 09/05/2015 and 08/12/2014; Worldview-2 on 17 July 2016).

This map illustrates satellite-detected areas potentially damaged and related density and the identified road obstacles in Jérémie city and surrounding areas in Grande Anse department, Haiti. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired on the 07 October 2016 and Worldview-1 image acquired on 01 May 2015 and 08 December 2014. The UNITAR-UNOSAT analysis in the outskirts of Jeremie combined with Copernicus analysis in Jeremie city, identified 13,013 potentially damaged structures within this map extent. The UNITAR-UNOSAT analysis identified 134 road obstacles and 800 population gathering sites within the map extent.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: EQ20210814HTI** This map illustrates potentially damaged structures/buildings in Baraderes commune, Nippes department of Haiti as detected by using a satellite image acquired after the earthquake. UNOSAT analysis used a Worldview-3 image acquired on 16 August 2021, and a Resurs-P image acquired on 18 August 2021, as a post-event images. Within the analyzed area, UNOSAT identified 7 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code: EQ20210814HTI** This report describes preliminary building damage analysis covering the departments of South, Grande Anse, and Nippes of Haiti, for a total area of approximately 550 Km2. A building damage analysis and a rapid assessment of transportation network conditions was conducted by comparing a series of satellite imageries acquired from the International Charter Space and Major Disasters, before and after the earthquake. The preliminary analysis extent within South, Grande Anse, and Nippes departments identify a total of 1,842 buildings/structures with visible damages and approximately 130 locations with visible road obstacles and/or access constraints.

Haiti Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Haiti Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

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 Haiti **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0525 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0031 million km (5.8369%) - Unpaved roads: 0.0109 million km (20.7147%) About 0.0385 million km (73.4485%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (0.3296%) 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

**UNOSAT code: EQ20210814HTI** This map illustrates potentially damaged structures/buildings in Chardonnieres commune, South department of Haiti as detected by Pleiades image acquired on 23 August 2021. Within the analyzed area, UNOSAT has identified 27 damaged structures, 107 potentially damaged structures, 9 landslide locations. 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).

No description yet.

No description yet.

No description yet.

No description yet.

**ADAM ID: 1000996\_7** Cyclone (category 1) during the period Aug 20 2023-Sep 01 2023 in Bermuda, Turks and Caicos Islands, Dominican Republic, Haiti. It impacted 0 people.

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.

Haiti Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

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

**UNOSAT code: EQ20210814HTI** This map illustrates potentially damaged structures/buildings in Nippes department of Haiti as detected by using a satellite image acquired after the earthquake. UNOSAT analysis used a Worldview-2 image acquired on 14 & 16 August 2021, a Worldview-1 image acquired on 15 August 2021, a Worldview-3 image acquired on 16 August 2021, and Pleiades images acquired on 15 & 16 August 2021, as a post-event images. Within the analyzed area, UNOSAT identified 73 damaged structures and 125 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

A gridded raster of population distribution for Haiti, with cell values representing population counts. Published by WorldPop (University of Southampton), it covers the year 2030.

A gridded raster of population distribution for Haiti, with cell values representing population counts. It covers the year 2030 and is published by WorldPop (University of Southampton).