Jamaica GIS data

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

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.

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.

Dates of Images: November 4, 2025; November 2, 2025; October 30, 2025 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology created maps of surface disturbance caused by Hurricane Melissa in Jamaica on October 28, 2025 using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the surface disturbance. The ARIA-share website has always focused on posting preliminary results as fast as possible for disaster response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. OPERA DIST-ALERT-HLS The Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the maximum vegetation anomaly value (VEG-ANOM-MAX) layer. All provided layers have been temporally filtered to include only disturbance detected following the start of Hurricane Melissa (starting October 28, 2025). Images are provided as GeoTIFF files. Note: Artifacts caused by clouds and cloud shadows are present in the product. The DIST-HLS product will eventually be updated to address these issues as more cloud-free data becomes available. VEG-ANOM-MAX: Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year. Layer values: 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8/9 satellite. Resolution: 30 meters The DIST-ALERT-HLS products included here have these flags: 255 represents ''No Data'' and is based on the Fmask layer of the source HLS granule. OPERA DIST-ALERT-HLS data availability: Note: some direct straight lines on the edges of domains or within the data may represent boundaries between satellites or satellite overpasses as this is a hybrid product from multiple distinct assets. The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251028_Hurricane_Melissa/DIST-HLS/ The OPERA DIST-HLS products have been in production since January 2022, are freely distributed to the public via NASA's Land Processes Distributed Active Archive Center (LP DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Surface Disturbance product suite, please refer to the DIST Product page: https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/ For more information about the GLAD project, visit https://glad.umd.edu/ For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/ Product POCs: Alexander Handwerger (alexander.handwerger@jpl.nasa.gov) Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Ines Fenni (ines.fenni@jpl.nasa.gov) Credits: The product contains modified Copernicus Sentinel data (2025) and is produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group. Managed by NASA's Jet Propulsion Laboratory, OPERA funds and manages the DIST-ALERT-HLS product developed and produced by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland. NASA JPL-Caltech-UMD ARIA/OPERA Team ================== Last Update 06 November 2025

Dates of Images: Post-Event: November 3, 2025 Pre-Event: October 20, 2025 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for November 2, 2025 as compared to October 20, 2025. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI), Landsat 8 Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/landsat_bNDVI/MapServer/WMSServer

uav imagery by DroneTM local Pilots, HOT, jamaica, workshop 2025, Flying labs, acquired 2025-08-13 to 2025-08-13, 1.9 cm per pixel, published on OpenAerialMap.

uav imagery by Phil at Hot, acquired 2025-08-14 to 2025-08-14, 1.9 cm per pixel, published on OpenAerialMap.

uav imagery by HOTOSM JFL, acquired 2025-11-05 to 2025-11-05, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by HOT-PH, acquired 2025-11-15 to 2025-11-15, 4.7 cm per pixel, published on OpenAerialMap.

uav imagery by PH, acquired 2025-11-15 to 2025-11-15, 9.5 cm per pixel, published on OpenAerialMap.

uav imagery by Philip Hippolyte at HOT, acquired 2025-11-20 to 2025-11-20, 4.7 cm per pixel, published on OpenAerialMap.

uav imagery by tanzania jamaica flyingLabs, esri, NERGIST, WeRobotics, HOT, Humanitarian, OSM, OpenStreetMap Team, CSOD, GeoTechVision, , acquired 2025-12-07 to 2025-12-07, 4.2 cm per pixel, published on OpenAerialMap.

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Jamaica Parish Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in September 2023. Other administrative boundaries for this country are also available:Country   County

This dataset is experimental and should only be used for situational awareness and data corroboration. Derived luminosity loss maps are processed data based off Black Marble BRDF nighttime light observations. This data may be used as a proxy for power loss. The original BRDF data is available at: https://gis.earthdata.nasa.gov/portal/home/item.html?id=654bc62d78fc4704923369a00a635937 Dates of Image: Pre-Event: September 2025 Post-Event: October 30, 2025-November 2, 2025; November 4, 2025; November 7, 2025; November 8, 2025; November 9, 2025 Methodology: Only a subset of the raw black marble data was used. Note that while the Disasters team has clipped data to remove false positives, some observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Certain areas were filtered out into an “Analyzed areas” map, which shows subsets of counties that were retained after certain data extents were removed: Baseline (August 2024) data with low radiance ( Cloud cover from the date of interest (in this example, September 29th) is removed from the county geometry. Counties are only included within the bounding box of the Black Marble data that was provided. Once the extent was finalized, the Black Marble Data was modified: Negative out-of-range values were changed to 0. The revised black marble pixels were extracted to the analyzed area geometry: The difference between pre and post-event total light intensity in each county was calculated to measure percent light intensity loss. This is indicated in popups as Light Intensity Loss (percentage). The percentage of analyzed area (with area omitted due to being background in the baseline or cloud-covered in the day of Black Marble) is also available in the attribute table. Lower values may reflect lower confidence in the percent Luminosity Loss depending on where the cloud cover is, especially in circumstances where the primary light source in a given geometry is obfuscated. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Note that these maps are experimental and subject to change and development and should be used only for situational awareness. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 SNPP: August 2024 composite, September 22-24, 2024, October 1, 2024 NOAA-20: September 25 - September 31, 2024, October 2, 2024 - October 4, 2024 Resolution: 500 meters Credits: NASA Black Marble Science team Disasters Response Coordination System Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/BM_PowerLossProxy/MapServer/WMSServer

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Jamaica County Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in September 2023. Other administrative boundaries for this country are also available:Country   Parish

Dates of Images: Post-Event: 11/2/2025 Pre-Event: 10/19/2025 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for November 2, 2025 as compared to October 20, 2025. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: PlanetScope  Resolution: 3 meters Credits: NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved. Esri REST Endpoint: See URL section on right side of page

Date of Images Co and Post-event: 2025-10-30; 2025-11-02; 2025-11-04 Pre-event: 2025-10-15, 2025-10-17, 2025-10-18, 2025-10-20, 2025-10-23 Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory and California Institute of Technology created maps of surface water extent impacted by the flooding caused by Hurricane Melissa in Jamaica on October 28, 2025 using the OPERA Dynamic Surface Water Extent (DSWx) from NASA Harmonized Landsat Sentinel-2 (HLS) product.  The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who wanted to have a rough first look at the inundation extent. The ARIA-share website has always focused on posting preliminary results as fast as possible for disaster response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind.  Use at your own risk. OPERA DSWx-HLS The OPERA DSWx-HLS was used to identify surface water for the flooding event. The images provided are a mosaic of OPERA DSWx-HLS products, producing a composite image for each date. The mosaicking applied prioritized pixels classified as open surface water to preserve water pixels in region of product granule overlap. The images are provided as GeoTIFF files.  Note: Cloud/cloud shadow and snow/ice layers are derived from HLS Fmask quality assurance (QA) data, which sometimes misclassifies sediment-rich water as snow/ice. Snow/ice pixels were reclassified to open water to capture the full innundated extent. Water (WTR) Layer Description: Masked interpreted water classification layer. This represents pixel-wise classification into one of three water classes (not water, open water, and partial surface water), snow/ice, cloud/cloud shadow and adjacent to cloud/cloud shadow, ocean masked, or no data classes as a result of processing the diagnostic (DIAG) layer through all intermediate steps/layers (WTR-1, and WTR-2). Water (WTR) Layer Values: 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in “not water” (class 0) where land cover masking is applied. (suggested color: #ffffff) 1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff) 2: Partial Surface Water – an area that is considered inundated, extracted from the high value in dual polarization ratio and the wetland class in land cover map. (suggested color: #b4d5f4) 252: Snow/Ice - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input  HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) Binary Water (BWTR) Layer Description: The binary water layer is derived from the WTR layer as a union of water classes (open water and partial surface water) into a binary map indicating areas with and without water. This layer is meant to provide users with a quick view for  water/no-water. Invalid data classes (snow/ice, cloud/cloud shadow along with adjacent to cloud/cloud shadow, ocean masked, and fill value) are also provided to indicate areas in which the binary classification does not provide water/no-water classification. Binary Water (BWTR) Layer Values: 0: Not Water - an area with valid reflectance data that is not water (class 1) and not snow/ice (class 252), cloud/cloud shadow  (class 253), or ocean masked (class 254) (suggested color: #ffffff) 1: Water - an area classified as open water or partial surface water (suggested color: #0000ff) 252: Snow/Ice - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input  HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Satellite/Sensor: Multi Spectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B/C satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8 satellite. Resolution: 30 meters OPERA DSWx-HLS data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251028_Hurricane_Melissa/DSWx-HLS/. The OPERA DSWx-HLS products have been in production since April 2023, are freely distributed to the public via NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Dynamic Surface Water eXtent product suite, please refer to the DSWx Product page: https://www.jpl.nasa.gov/go/opera/products/dswx-product-suite.  For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov.  For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/.   REST Endpoint: See the URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/ARIA_DSWxHLS/MapServer/WMSServer Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Ines Fenni (ines.fenni@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Credits: HLS data was accessed through NASA EarthData Search. The product contains modified Copernicus Sentinel data (2025), processed by the European Space Agency, NASA/USGS Landsat 8/9 data, and analyzed by the NASA-JPL/Caltech ARIA and OPERA team. The products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA Jet Propulsion Laboratory. NASA JPL-Caltech ARIA/OPERA Team ================== Last Update 29 Oct 2025

uav imagery by HOT OSM Humanitarian Open Streetmap Team- PH, acquired 2025-11-14 to 2025-11-20, 4.7 cm per pixel, published on OpenAerialMap.

uav imagery by Wingtra, HOT, JFL, FLying Labs, WeRobotics, Nergist, Esri, CSOD, , acquired 2025-11-17 to 2025-11-17, 1.9 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: November 2, 2025; November 4, 2025; November 6, 2025 Pre-Event: October 20, 2025 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for November 2, 2025 as compared to October 20, 2025. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Jamaica Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.  The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in September 2023. Other administrative boundaries for this country are also available:County   Parish

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.

Population Exposure Wind Speed Zones Matthew-16

**ADAM ID: **1001230\_41 Category 1 during the period Oct 21 2025-Oct 31 2025 in Jamaica, Cuba, Bahamas, Bermuda, Haiti, Canada. The center of the storm was located near latitude 39.0 longitude -60.5. 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.

**UNOSAT code: TC20251028JAM, GDACS ID: 1001230** This map illustrates the structures and buildings damaged by the passage of Hurricane MELISSA-25. The analysis focuses on White House Village in Westmoreland Parish, as of 31 October 2025. Damage and destruction were identified using very highresolution aerial imagery acquired by NOAA on 31 October 2025. In this area, UNOSAT identified a total of 1,875 affected buildings, of which 715 were completely destroyed and 1,160 were damaged. This is a preliminary analysis and has not yet been validated in the field. Ground verification is encouraged, and feedback can be sent to the United Nations Satellite Centre (UNOSAT).

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**UNOSAT code: TC20251026HTI, GDACS ID: 1001230** This map illustrates the structures/buildings damaged by Tropical Cyclone MELISSA-25. The analysis focuses on Crawford Village, Black River District, Saint Elizabeth Parish, as of 29 October 2025, where damage was detected using a WorldView-2 very high-resolution satellite image acquired on 29 October 2025. UNOSAT identified approximately 1,089 buildings as 100% damaged. This is a preliminary analysis and has not yet been validated in the field. Ground verification is encouraged, and feedback can be sent to the United Nations Satellite Centre (UNOSAT).

**ADAM ID: 1001067\_27** Cyclone (category 1) during the period Jun 28 2024-Jul 06 2024 in Haiti, Grenada, Jamaica, Trinidad and Tobago, Saint Vincent and the Grenadines, Mexico, Belize, United States. It impacted 0 people.

**UNOSAT code: TC20251026HTI, GDACS ID: 1001230** This map illustrates structures and buildings damaged by Tropical Cyclone MELISSA-25. The analysis focuses on Black River Village, Saint Elizabeth Parish, as of 29 October 2025. Damage and destruction were identified using a WorldView-2 very high-resolution satellite image acquired on 29 October 2025. UNOSAT identified approximately 314 destroyed and 1,183 damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Ground verification is encouraged, and feedback can be sent to the United Nations Satellite Centre (UNOSAT).

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.

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

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).