Brazil GIS data
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uav imagery by Gabriel Mancilla, acquired 2026-09-05 to 2026-09-05, 5.3 cm per pixel, published on OpenAerialMap.
Date of Images: 5/15/2024, 5/8/2024, 4/21/2024, 4/6/2024 Summary: Scientists at NASA's Marshall Space Flight Center created these water extents using Landsat imagery. These images can be used to see where open water is visible at the time of the satellite overpass. This product shows all water detected and differentiates between normal water areas and some flooded areas. This product was classified using the ESA Worldview. It's important to note that all flooded areas may not be captured do to the sensors limitations of not being able to "see" through vegetation and buildings. To determine where additional flooding may have occurred, combine this layer with other data sets. Suggested Use: This product shows water that is detected by the sensor with different colors indicating different land cover/land use classifications from CDL that appear to have water and are potentially flooded. Blue (1): Known Water Red (2): Flooded Developed Green (3): Flooded Vegetation Orange (4): Flooded Cropland/Grassland Gray (5): Clouds/Cloud Shadow (0): No Data Satellite/Sensor: Landsat Resolution: 30 meters Credits: NASA Disasters Program, Includes copyrighted material of Planet Labs PBC. All rights reserved. Esri REST Endpoint: See URL section on the right side of page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_landsat_WaterExtent/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. Brazil Sectore Boundaries provides a 2021 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 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from 2013 Instituto Brasileiro de Geografia e Estatistica. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Country Unidade Municipio Distrito Subdistrito
Dates of Images: 20220422T1234, 20220427T1242, 20220501T0015, 20220504T1234, 20220509T1242, 20220516T1234, 20220528T1234 (GMT) (YYYYMMDD: HHMM) 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 produced flood mapping products using the OPERA Dynamic Surface Water Extent from Sentinel-1 (DSWx-S1) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provided includes three products: Surface Water Extent maps on individual dates Water Gain maps, showing areas where water increased between two dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Pre-event image: April 22, 2022 Flood images: April 27, 2022, May 01, 2022, May 05, 2022, May 16, 2022, May 28, 2022 Products are generated from OPERA DSWx-S1 satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description: Surface water is identified from Synthetic Aperture Radar (SAR) satellite imagery and mapped at 30 m spatial resolution. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations: Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency 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. Suggested Use: The OPERA DSWx-S1 Water product classifies the Sentinel-1 (S1) input imagery into "not water", "open surface water", and "inundated vegetation”, masks include Height Above Nearest Drainage (HAND) mask and layover/shadow mask), or no data classes. Water (WTR) Layer Values: File names:, OPERA_L3_DSWX-S1_V1_WTR_20220528T1234_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220516T1234_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220509T1242_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220504T1234_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220501T0015_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220427T1242_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20220422T1234_mosaic.tif 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) (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) Maximum Water Extent Layer Values: The maximum water extent observed during the event was created by aggregating OPERA DSWx-S1 water products. File names: ARIA-OPERA_L3_DSWX-S1_V1_WTR_20220422T1234_20220601T0007_max_extent.tif 0: Not Water. No water was detected within the time period of the images. Masked pixels are set to No data. (suggested color: #ffffff) 1: Water - an area where open water or partial surface water was detected during the time period of the images. (suggested color: #ff15e4) Water Gain Layer Values: The water gain shows any location where there was a change from “not water” to “inundated vegetation”, “not water” to “open surface water”, or “inundated vegetation” to “open surface water" between two OPERA DSWx-S1 products. File names: ARIA-OPERA_L3_DSWX-S1_V1_WTR_20220422T1234_20220504T1234_water_gain.tif 0: No Change or Water Loss- an area that had no change (e.g., open water in both images) or water loss (e.g., open water to not water). Masked pixels are set to No data. (suggested color: #ffffff) 1: Water Gain - an area that gained water in the most recent image. This includes change from Not Water to Inundated Vegetation, Not Water to Open Water, and Inundated Vegetation to Open Water. (suggested color: #ff15e4) Credits: The product contains modified Copernicus Sentinel data, processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DSWx 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. Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Emre Havazli (emre.havazli@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_opera_dswx_s1/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. Brazil Unidade Boundaries provides a 2021 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 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from 2013 Instituto Brasileiro de Geografia e Estatistica. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Country Municipio Distrito Subdistrito Sectore
Dates of Images: May 1, 2024-May 7, 2024 Summary: Storms that produce heavy rain, damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks. Suggested Use: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Satellite/Sensor/Resolution: GOES-R satellite channels 13 and 15 at approximately 2 km resolution and 5-10 mins. There were some issues with some of the cases GOES files so there are times in which they were skipped. GFS tropopause temperature was also used. Data are all interpolated to 0.5 km resolution. Credits: If you use this software in your project, please contact our team prior to publication and cite our contribution. When using this software, credit the technical background and validation for this software as described here: Cooney, J. W., Bedka, K. M., Liles, C. A., and Homeyer, C. R. (in review). Automated Detection of Overshooting Tops and Above Anvil Cirrus Plumes Within Geostationary Imagery Using Deep Learning. Artificial Intelligence for the Earth Systems. Code is available at https://github.com/nasa/svrstormsig. Esri REST Endpoint: See URL to the right WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_severestorm/MapServer/WMSServer
aircraft imagery by Esteio, acquired 2025-08-16 to 2025-08-16, 73.8 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-07-15 to 2025-07-15, 9.4 cm per pixel, published on OpenAerialMap.
aircraft imagery by Esdras de Lima Andrade, acquired 2025-08-16 to 2025-08-16, 250.0 cm per pixel, published on OpenAerialMap.
satellite imagery by Alfredo Queiroz, acquired 2025-09-02 to 2025-09-02, 19.1 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-09-05 to 2025-09-05, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2025-10-08 to 2025-10-08, 5.6 cm per pixel, published on OpenAerialMap.
Frequência de atualização: Mensal Dados extraídas da base do Imposto Sobre Serviços de Qualquer Natureza (ISSQN) - Estabelecimentos Abertos por Logradouro, Bairros e Grupos de Atividade, segundo o Ano de Concessão.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-08-21 to 2025-08-21, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2026-06-12 to 2026-06-13, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by UFBA; SEMIT; CODESAL e SEINFRA, acquired 2025-06-30 to 2025-06-30, 3.0 cm per pixel, published on OpenAerialMap.
satellite imagery by COMDEMA, acquired 2026-06-01 to 2026-06-30, 477.2 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM2 boundaries for Brazil: 5570 units, representing 2020. Source: Instituto Brasileiro de Geografia e Estatística (IBGE), OCHA ROLAC.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-03-18 to 2025-03-18, 9.4 cm per pixel, published on OpenAerialMap.
satellite imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-04-04 to 2025-04-04, 10.0 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-04-25 to 2025-04-25, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-06-10 to 2025-06-10, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-08-01 to 2025-08-01, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-08-08 to 2025-08-08, 9.4 cm per pixel, published on OpenAerialMap.
aircraft imagery by Esdras de Lima Andrade, acquired 2025-08-15 to 2025-08-15, 250.0 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-08-15 to 2025-08-15, 9.4 cm per pixel, published on OpenAerialMap.