Mexico GIS data

Dates of Images: July 9, 2025 Date of Next Image: None Expected Summary: These maps show the results of a machine learning classification applied to UAVSAR data to identify open water flooding and inundation under tree canopy.  Colored areas in the image show detected flooding, where the color shows the type of landcover which was flooded (see legend below).  Non-flooded areas are transparent.  The classification uses a U-Net machine learning algorithm which was trained using UAVSAR data from previous flood events.  UAVSAR provides polarimetric synthetic aperture radar data from which different types of scattering mechanisms can be observed, which provides information on the type of flooding.  For example, flooding underneath vegetation produces strong double-bounce scattering from the water surface and tree trunks.  However, strong double-bounce scattering from urban areas aligned with the radar viewing direction, such as in Austin, TX, can produce erroneous classifications.  Non-flooded vegetation is generally dominated by volume scattering from the forest canopy.  In comparison, open water flooding exhibits weak radar returns in all polarizations. Suggested Use: Classified Images: Blue   = Open Water Yellow = Flooded Developed Green  = Flooded Vegetation Orange = Flooded Crop Satellite/Sensor: UAVSAR airborne L-band synthetic aperture radar (SAR) aboard a NASA Gulfstream C-20A jet. Resolution: 10 meters Credits: UAVSAR data courtesy of NASA/JPL-Caltech Esri REST Endpoint: See URL section on right side of the page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202507_FLOOD_TX/202507_UAVSAR/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl

aircraft imagery by Jose Reyes, acquired 2026-07-14 to 2026-07-14, 1.0 cm per pixel, published on OpenAerialMap.

uav imagery by Perry Smith, acquired 2026-02-28 to 2026-02-28, 12.0 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2026-02-08 to 2026-02-08, 4.0 cm per pixel, published on OpenAerialMap.

aircraft imagery by Luis Sierra, acquired 2026-02-19 to 2026-02-19, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Urbainilla, acquired 2026-04-21 to 2026-04-21, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Netas Ciudadanas, acquired 2026-09-12 to 2026-09-13, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-04-02 to 2025-04-02, 5.0 cm per pixel, published on OpenAerialMap.

Storm surge hazard is modeled only along the coastline. The process involves generating a grid along the coastline and 5km inland. In fact, no backwater effects were modeled, this is only due to the continent’s contour map (shapefile) version used to create the grid mentioned on the storm surge hazard map indicates the mean run-up height on the coast for seven return periods: 10, 25, 50, 100, 250, 500, 1000 years. The map has coastal coverage with points in the countries with storm surge hazard coastline. The data were classified into three hazard categories—low, moderate, and high—based on storm surge run-up heights for a 250-year return period, a widely used standard for coastal hazard zoning and infrastructure risk assessment. Run-up values below 0.1 meters are considered as “Low”; run-up values between 0.1 meters and 2 meters (inclusive) are considered as “Moderate”; run-up values greater than 2 meters are considered as “High”. In this project Mediterranean tropical-like cyclones were not included in the hazard assessment process. We considered only tropical cyclones as storms that develop in areas of low pressure over tropical oceans, and that are associated with several hazards such as strong winds and storm surge. These two hazards were assessed at a global level but given that although tropical cyclones are formed along the North Atlantic Ocean but do not occur in Europe, neither strong wings nor storm surge were modeled for European countries. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.

uav imagery by geografia_en_viz, acquired 2025-05-20 to 2025-05-20, 5.6 cm per pixel, published on OpenAerialMap.

uav imagery by Municipio de Lerma , acquired 2026-03-11 to 2026-03-11, 8.5 cm per pixel, published on OpenAerialMap.

uav imagery by CNPC, acquired 2026-03-24 to 2026-03-24, 10.0 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, acquired 2026-05-22 to 2026-05-22, 14.3 cm per pixel, published on OpenAerialMap.

satellite imagery by Jose Reyes , acquired 2026-06-25 to 2026-06-25, 5.0 cm per pixel, published on OpenAerialMap.

aircraft imagery by mase, acquired 2026-06-28 to 2026-06-28, 5.0 cm per pixel, published on OpenAerialMap.

Date of Images: 05/18/2024 Summary: The visible-wavelength true color RGB digital camera images were taken by astronauts on board the International Space Station showing flooding in southeastern Texas. These images were manually georeferenced by members of the Earth Science and Remote Sensing Unit at NASA Johnson Space Center. Suggested Use: This image provides regional context, and may be useful for visualization of the ongoing event. Higher spatial resolution images may be suitable for spatial analysis to support decision making or research applications. Sensor: Nikon Z9 Electronic Still Camera. Additional Information about the astronaut photography from the ISS. Credits: International Space Station Program, NASA Johnson Space Center Earth Science and Remote Sensing Unit, ARES Division, Exploration Architecture, Integration & Science Directorate Data Download: Full datasets are available for download at the Gateway to Astronaut Photography of Earth. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=c9bb2094473649b2a3802305f4fc8eb1 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_TX/2408_iss/ImageServer/WMSServer

uav imagery by WebODM, acquired 2025-12-30 to 2025-12-30, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, Netas ciudadas, uam cuajimalpa, Hot OSM, acquired 2025-10-12 to 2025-10-12, 9.3 cm per pixel, published on OpenAerialMap.

uav imagery by Said Figueroa, acquired 2025-08-02 to 2025-08-02, 1.7 cm per pixel, published on OpenAerialMap.

uav imagery by Seth Anderson, acquired 2025-09-01 to 2025-09-02, 4.1 cm per pixel, published on OpenAerialMap.

uav imagery by personal, acquired 2026-04-17 to 2026-04-18, 3.9 cm per pixel, published on OpenAerialMap.

uav imagery by Ayuntamiento de Lerma, acquired 2026-04-29 to 2026-04-29, 3.8 cm per pixel, published on OpenAerialMap.

uav imagery by Comapper, acquired 2026-04-29 to 2026-04-29, 4.1 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-09-28 to 2025-09-28, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Netas Ciudadanas, acquired 2025-09-29 to 2025-09-29, 5.4 cm per pixel, published on OpenAerialMap.

Plat tracker easement features linked to boundary applications, with each feature carrying an application number, review cycle, application code and status, subdivision name and section, lot and reserve counts, reserve size, and upload and plat check dates. Published by cohgis_ago.

The storm surge hazard map indicates the mean run-up height on the coast for seven return periods: 10, 25, 50, 100, 250, 500, 1000 years. The map has coastal coverage with points in the countries with storm surge hazard. The data were classified into three hazard categories—low, moderate, and high—based on storm surge run-up heights for a 250-year return period, a widely used standard for coastal hazard zoning and infrastructure risk assessment. Run-up values below 0.1 meters are considered as “Low”; run-up values between 0.1 meters and 2 meters (inclusive) are considered as “Moderate”; run-up values greater than 2 meters are considered as “High”. Storm surge hazard is modeled only along the coastline. The process involves generating a grid along the coastline and 5km inland. In fact, no backwater effects were modeled, this is only due to the continent’s contour map (shapefile) version used to create the grid mentioned on the coastline. The tropical cyclone events used for the elaboration of this map consider climate change based on models representative concentration pathways (RCP) scenarios. Climate change scenario: SSP5 RCP8.5 In this project Mediterranean tropical-like cyclones were not included in the hazard assessment process. We considered only tropical cyclones as storms that develop in areas of low pressure over tropical oceans, and that are associated with several hazards such as strong winds and storm surge. These two hazards were assessed at a global level but given that although tropical cyclones are formed along the North Atlantic Ocean but do not occur in Europe, neither strong wings nor storm surge were modeled for European countries. Background paper: Multi-hazard Disaster Risk Model of Infrastructure and Buildings at the Global Level (2023). Cardona, O.D., Bernal, G.A., Villegas, C.P., Molina, J.F., Herrera, S.A., Marulanda, M.C., Rincón, D.F., Grajales, S., Marulanda, P.M., Gonzalez, D., Maskrey, A. (2023). https://giri.unepgrid.ch/sites/default/files/2023-11/2.4-INGENIAR-CDRI-Background-Report-Risk-model.pdf Release date: Fri Jun 30 2023 The layer was clipped to the geographic boundaries of 1) IDB Borrowing Member Countries, 2) Caribbean Development Bank (CDB) Borrowing Countries, 3) Organization of Eastern Caribbean States (OECS) Member Countries.

uav imagery by WebODM, acquired 2026-01-14 to 2026-01-14, 2.2 cm per pixel, published on OpenAerialMap.

satellite imagery by Samuel Cardona Garza, acquired 2026-06-24 to 2026-06-25, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Patlani Cartografía, acquired 2025-10-13 to 2025-10-13, 4.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Jose Reyes, acquired 2026-06-25 to 2026-06-25, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Netas Ciudadanas, acquired 2026-07-19 to 2026-07-19, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Netas Ciudadanas, acquired 2025-10-19 to 2025-10-20, 5.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM0 boundaries for Mexico: 1 units, representing 2012. Source: geoBoundaries, World Bank.

satellite imagery by WEBODM, acquired 2025-04-10 to 2025-04-10, 5.0 cm per pixel, published on OpenAerialMap.

Dates of Images: Post-Event: July 1, 2026; July 3, 2026; July 5, 2026 Pre-Event: May 14, 2026 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