Brazil GIS data
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uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-10-17 to 2025-10-17, 9.4 cm per pixel, published on OpenAerialMap.
aircraft imagery by JONNATHAN NOGUEIRA, acquired 2026-02-11 to 2026-02-12, 1.8 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-01-14 to 2026-01-14, 3.6 cm per pixel, published on OpenAerialMap.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. Brazil Distrito 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 Subdistrito Sectore
uav imagery by Drone Mapper Inspeção e Mapeamento LTDA, acquired 2026-02-07 to 2026-02-07, 3.6 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. Brazil Subdistrito 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 Sectore
aircraft imagery by jonnathan, acquired 2026-03-10 to 2026-03-10, 1.8 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-07-26 to 2026-07-26, 2.6 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2026-06-26 to 2026-06-26, 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.
Dates of Images: 20240508T2206, 20240511T0849 (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 two products: Surface Water Extent maps on individual dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Flood images: May 08, 2024 and May 11, 2024 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_20240508T2206_mosaic.tif, OPERA_L3_DSWX-S1_V1_WTR_20240511T0849_mosaic.tif Water (WTR) 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) (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_20240508T2206_20240511T0849_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 inundated vegetation was detected during the time period of the images. (suggested color: #ff15e4) Satellite/Sensor/Resolution: Satellite/Sensor: Synthetic Aperture Radar (SAR) instrument on European Space Agency's (ESA) Sentinel-1A/C satellites. Spatial Resolution: 30 meters Temporal Resolution: sub-daily to bi-weekly 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 to the bottom right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_opera_dswx_s1/MapServer/WMSServer
Frequência de atualização: Mensal (por motivos ligados à limpreza da base de dados bruta do ITBI, a partir de junho de 2026 os dados desta tabela não contém os registros do último mês). Foram considerados o número total de transações no mês, para as Naturezas de Compra e Venda e de Promessa de Compra e venda, as médias de área construída, as médias de percentual do imóvel transacionado, a média do valor da transação e a média dos valores totais do imóvel, apurado pela relação entre o percentual transacionado e o valor da transação. Não estão contabilizadas as demais Naturezas de Transações, não tendo esta tabela o total de Transações imobiliárias. Porém este total perfaz valor acima de 95% do total das Transações.
Natural Earth admin 0 countries bra at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.
Dates of Images: Dates: 04/20/2024 (pre-flood), 05/06/2024, 05/07/2024, 05/09/2024, 05/15/2024, 05/21/2024 Time Zone: GMT (local crossing time of ~1:00 pm) Summary: These are NDVI images from PACE OCI of the 2024 flooding in Brazil using data from the OCI LANDVI suite, where NDVI is one of the products delivered daily by the mission. The images show how the landscape changes from a pre-flood day (04/20/2024) through flood days ending at 05/21/2024 at ~1:00 pm local time. NDVI is a measure of approximate greenness and health in vegetation but can also be useful in distinguishing water extent and unhealthy vegetated areas, which can be see in these images throughout the flood. Suggested Use: NDVI is a normalized band ratio measuring approximate greenness and vegetation health. Higher positive values mean greener and abundant vegetation, values around 0 mean bare soil or rock, and lower negative values mean non-vegetated areas like water or clouds. These images show the transition of the area around the Jacui River from non-flooded conditions (high NDVI values) to flooded (NDVI values in the negative, indicating water presence). Clouds have been screened from these products, so low NDVI values can around the river can be identified as water. Satellite/Sensor/Resolution: Satellite: PACE, sensor: OCI, temporal resolution: daily, spatial resolution: gridded to 0.015 degrees from a Level 2 product in instrument swath (varying pixel size, 1.2 km at nadir).
Limites AdministrativosA camada conta com as informações geográficas na seguinte ordem: Código do Setor Censitário, bairro, codbairro, regiao_adm, codra, ra, rp, cod_rp, ap. Camada com os dados censitários relativos ao Censo Demográfico de 2022. Para compor as informações foram utilizados os seguintes dicionários: BásicoTotal de pessoas Total de Domicílios (DPPO + DPPV + DPPUO + DPIO + DCCM + DCSM) Total de Domicílios Particulares (DPPO + DPPV + DPPUO + DPIO) Total de Domicílios Coletivos (DCCM + DCSM) Total de Domicílios Particulares Ocupados (DPPO + DPIO) Cor ou Raça Cor ou raça é branca Cor ou raça é preta Cor ou raça é amarela Cor ou raça é parda Cor ou raça é indígena DemografiaSexo masculino Sexo feminino 0 a 4 anos 5 a 9 anos 10 a 14 anos 15 a 19 anos 20 a 24 anos 25 a 29 anos 30 a 39 anos 40 a 49 anos 50 a 59 anos 60 a 69 anos 70 anos ou mais ParentescoDomicílio com responsável e cônjuge(s) sem filhos, Sexo da pessoa responsável pelo domicílio é masculino Domicílio com responsável e cônjuge(s) sem filhos, Sexo da pessoa responsável pelo domicílio é feminino Domicílio com responsável e cônjuge com filho de ambos somente, Sexo da pessoa responsável pelo domicílio é masculino Domicílio com responsável e cônjuge com filho de ambos somente, Sexo da pessoa responsável pelo domicílio é feminino Domicílio com responsável e cônjuge(s) com pelo menos um filho somente do responsável ou somente de um(a) cônjuge, Sexo da pessoa responsável pelo domicílio é masculino Domicílio com responsável e cônjuge(s) com pelo menos um filho somente do responsável ou somente de um(a) cônjuge, Sexo da pessoa responsável pelo domicílio é feminino Domicílio com responsável sem cônjuge com filho(s) e/ou enteado(s), Sexo da pessoa responsável pelo domicílio é masculino Domicílio com responsável sem cônjuge com filho(s) e/ou enteado(s), Sexo da pessoa responsável pelo domicílio é feminino Domicílio com outros tipos de composição, Sexo da pessoa responsável pelo domicílio é masculino Domicílio com outros tipos de composição, Sexo da pessoa responsável pelo domicílio é feminino Unidade em Domicílio Coletivo Com Morador, Sexo da pessoa responsável pelo domicílio é masculino Unidade em Domicílio Coletivo Com Morador, Sexo da pessoa responsável pelo domicílio é feminino RendaValor do rendimento nominal médio mensal das pessoas responsáveis com rendimentos por domicílios
Dates of Images: Post-Event: 5/6/2024 Pre-Event: 4/21/2024 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 May 6, 2024 compared to April 21, 2024. 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 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_sentinel2_bndvi/MapServer/WMSServer
Date of Images: 5/5/2022 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: Copernicus Sentinel-2 Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus 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_sentinel2_WaterExtent/MapServer/WMSServer
Visão geral Este serviço reúne informações territoriais sobre favelas acompanhadas por indicadores sociais e de condições de vida associados ao Índice de Desenvolvimento Social (IDS) para o ano 2000. O conteúdo apoia a leitura espacial das desigualdades urbanas e a identificação de padrões sociais em áreas de favela. Conteúdo da camada A camada principal representa polígonos de favelas e consolida atributos sobre perfil socioeconômico, condições domiciliares e acesso a serviços básicos. Os dados incluem medidas relacionadas a abastecimento de água, esgotamento sanitário, coleta de lixo, composição e renda das famílias, escolaridade e outros componentes usados na avaliação do desenvolvimento social. Aplicações Análise territorial de vulnerabilidade social em áreas de favela. Leitura espacial de indicadores de desenvolvimento humano e urbano. Apoio a diagnósticos e planejamento de políticas públicas voltadas à melhoria das condições de vida. Uso em visualização, consulta e integração com projetos GIS sobre desigualdade urbana.
uav imagery by Gabriel Mancilla, acquired 2026-01-13 to 2026-01-13, 3.5 cm per pixel, published on OpenAerialMap.
Visão geral Este serviço de feições reúne informações do Índice de Desenvolvimento Social por Região Administrativa, com recorte temporal de 2000. O conteúdo representa uma camada temática voltada à análise socioeconômica territorial, permitindo visualizar e comparar indicadores sociais em cada região administrativa. Conteúdo da camada Regiões Administrativas: geometria territorial com identificação da região e seus respectivos atributos. Indicadores sociais: valores relacionados a condições de infraestrutura domiciliar, escolaridade, renda e acesso a serviços, organizados em índices temáticos e em um índice geral. Aplicações Análise espacial de desigualdades e desenvolvimento social. Comparação entre regiões administrativas em estudos urbanos e territoriais. Suporte à elaboração de diagnósticos, planejamento público e políticas sociais. Uso em aplicativos e mapas temáticos para consulta e visualização de indicadores.
aircraft imagery by Gabriel Mancilla, acquired 2025-11-28 to 2025-11-28, 5.0 cm per pixel, published on OpenAerialMap.
