Mexico GIS data

satellite imagery by Netas Ciudadanas, acquired 2026-05-24 to 2026-05-24, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by BCSicletos y La Duna Centro Ecológico, acquired 2025-03-26 to 2025-03-27, 5.3 cm per pixel, published on OpenAerialMap.

uav imagery by BCSicletos y La Duna Centro Ecológico, acquired 2025-03-26 to 2025-03-27, 5.3 cm per pixel, published on OpenAerialMap.

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

uav imagery by Mar Marín, acquired 2025-09-24 to 2025-09-24, 5.4 cm per pixel, published on OpenAerialMap.

uav imagery by Municipio de Lerma , acquired 2025-09-23 to 2025-09-26, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Netas Ciudadanas, acquired 2025-10-12 to 2025-10-12, 5.6 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-10-13 to 2025-10-13, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by geografia_en_viz, acquired 2025-10-14 to 2025-10-15, 13.0 cm per pixel, published on OpenAerialMap.

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

uav imagery by WebODM, acquired 2025-12-18 to 2025-12-18, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, acquired 2025-11-02 to 2025-11-02, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Perry Smith, acquired 2026-02-09 to 2026-02-09, 12.5 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, acquired 2025-07-19 to 2025-07-20, 11.5 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, acquired 2025-08-21 to 2025-08-21, 12.8 cm per pixel, published on OpenAerialMap.

Date of Images Post-event: 2026-06-21, 2026-06-28 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 (JPL) and the California Institute of Technology produced maps showing areas affected by flooding in Louisiana using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) product. These maps provide an initial view of the surface water extent observed following the flood event. The results presented here are preliminary and unvalidated, intended primarily to support situational awareness and field response efforts. The ARIA-Share platform emphasizes rapid dissemination of satellite-based products for disaster response. All information is provisional and should be used under emergency response guidelines. These data are provided without warranty of any kind. Use at your own risk. OPERA DSWx-S1 The OPERA DSWx-S1 was used to identify surface water for the flooding event. The images provided are a mosaic of OPERA DSWx-S1 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. 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 inundated vegetation), masks (HAND mask and layover/shadow mask), or no data classes. Water (WTR) and Binary Water (BWTR) Layer Values: 0: Not Water - an area with valid data that is not open water (class 1), inundated vegetation (class 3), height above nearest drainage masked (class 250), or layover/shadow masked (class 251). 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) 3: Inundated vegetation - 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: #00ff00) 250: Height Above Nearest Drainage (HAND) masked - an area where topographic height is higher than the HAND threshold (suggested color: #c8c8c8) 251: Layover/shadow masked - an area identified as layover or shadow computed from the geometry of the digital elevation model and sensor. The area is directly copied from input burst RTC-S1 products (suggested color: #808080) 255: No data (suggested color: transparent) Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A satellite. Resolution: 30 meters OPERA DSWx-S1 data availability The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20251012_Alaska_Floods/DSWx-S1/. The OPERA DSWx-S1 products have been in production since September 2024, 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 URL on the right. Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (mary.grace.p.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: Sentinel-1 data were accessed through the Copernicus Open Hub and the Alaska Satellite Facility server. The product contains modified Copernicus Sentinel data (2026), processed by the European Space Agency 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's Jet Propulsion Laboratory. NASA JPL-Caltech ARIA/OPERA Team ==================

uav imagery by BCSicletos, acquired 2025-05-08 to 2025-05-09, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by geografia_en_viz, acquired 2026-07-04 to 2026-07-04, 15.8 cm per pixel, published on OpenAerialMap.

satellite imagery by Netas Ciudadanas, acquired 2026-02-27 to 2026-02-27, 9.2 cm per pixel, published on OpenAerialMap.

uav imagery by Seth Anderson, acquired 2025-08-25 to 2025-08-26, 3.6 cm per pixel, published on OpenAerialMap.

uav imagery by Alvaro Quijano_Alfonso Solis, acquired 2025-09-27 to 2025-09-27, 11.1 cm per pixel, published on OpenAerialMap.

satellite imagery by geografia_en_viz, acquired 2026-03-25 to 2026-03-25, 12.8 cm per pixel, published on OpenAerialMap.

uav imagery by Netas Ciudadanas, acquired 2026-08-30 to 2026-08-30, 5.0 cm per pixel, published on OpenAerialMap.

A High Injury Network (HIN) is a strategic tool used in urban planning and traffic management to identify and address road segments with high frequencies of traffic-related injuries and fatalities. The concept of HIN is rooted in the data-driven approach of Vision Zero, a multi-national road traffic safety project that aims to achieve a highway system with no fatalities or serious injuries involving road traffic. By mapping out the corridors where a disproportionate number of traffic crashes occur, HINs provide a clear picture of where interventions are most needed to enhance safety for all road users.

uav imagery by Mangles del Manglito, acquired 2025-04-01 to 2025-04-02, 5.0 cm per pixel, published on OpenAerialMap.

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

uav imagery by BCSicletos y La Duna Centro Ecológico, acquired 2025-03-26 to 2025-03-27, 5.0 cm per pixel, published on OpenAerialMap.

Plat tracker row 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.

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 Red    = 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://maps.disasters.nasa.gov/ags03/services/texas_flood_202507/uavsar/MapServer/WMSServer Data Download: https://uavsar.jpl.nasa.gov/cgi-bin/download-files.pl

uav imagery by bcsicletos, acquired 2025-03-25 to 2025-03-25, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by Jose Reyes, acquired 2026-07-14 to 2026-07-14, 5.0 cm per pixel, published on OpenAerialMap.

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

uav imagery by bcsicletos, acquired 2025-03-25 to 2025-03-25, 5.0 cm per pixel, published on OpenAerialMap.

satellite imagery by geografia_en_viz, acquired 2025-12-11 to 2025-12-11, 13.7 cm per pixel, published on OpenAerialMap.

uav imagery by geografia_en_viz, acquired 2025-08-24 to 2025-08-25, 8.4 cm per pixel, published on OpenAerialMap.

uav imagery by Karson A. Kall, acquired 2026-08-07 to 2026-08-07, 1.7 cm per pixel, published on OpenAerialMap.