Brazil GIS data
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uav imagery by Alice Longhi Branco; Roberto Fabris Goerl , acquired 2025-05-15 to 2025-05-16, 0.9 cm per pixel, published on OpenAerialMap.
Visão geral Este serviço apresenta a distribuição do Índice de Desenvolvimento Social (IDS) por bairro, com atributos associados à caracterização territorial e social das áreas urbanas para o ano 2000. O conteúdo reúne informações de bairros e suas referências administrativas, apoiando a leitura espacial das condições sociais em escala local. Conteúdo da camada Índice de Desenvolvimento Social por bairro: geometria de bairros com indicadores sociais agregados, incluindo o IDS e componentes relacionados a saneamento, escolaridade e renda. Identificação territorial: campos de apoio para vínculo com bairro, Região Administrativa, Região de Planejamento, Área de Planejamento e Superintendência, facilitando consultas e análises por recorte administrativo. Uso e aplicações Análise espacial de desigualdades sociais em nível de bairro. Leitura integrada de condições de saneamento, educação e renda. Apoio a diagnósticos territoriais, planejamento urbano e políticas públicas.
uav imagery by Gabriel Mancilla, acquired 2026-08-15 to 2026-08-15, 5.5 cm per pixel, published on OpenAerialMap.
uav imagery by Rkieferbaum, acquired 2026-03-03 to 2026-03-03, 4.0 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.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 1.7 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 3.3 cm per pixel, published on OpenAerialMap.
uav imagery by Prefeitura, acquired 2025-09-17 to 2025-09-17, 8.9 cm per pixel, published on OpenAerialMap.
uav imagery by WMB, acquired 2026-08-21 to 2026-08-21, 5.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 3.5 cm per pixel, published on OpenAerialMap.
uav imagery by WebODM, acquired 2025-11-10 to 2025-11-10, 5.0 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóibrega Borges, acquired 2025-09-16 to 2025-09-16, 3.4 cm per pixel, published on OpenAerialMap.
Date of Images: 5/6/2024 Date of Next Image: Unknown Summary: The floodwater depth raster produced for the 06th May 2024 for long-term floods in southern Brazil was generated using the Flood Water Depth Estimation Tool (Cohen et al, 2018, 2019; Peter et al, 2020). The Floodwater Depth Estimation Tool (FwDET) is a solution for producing timely floodwater depth data during flood activations that require emergency response and post-flood assessment. FwDET is based solely on a flood extent layer and a digital elevation model (DEM). The DEM used here was the NASA SRTM data. The flood extent layer was derived from the OPERA Dynamic Surface Water eXtent (DSWx) from the Harmonized Landsat Sentinel-2 (HLS) product. The DEM used was the NASA Shuttle Radar Topography Mission (SRTM) data. The computation involves identifying elevations at the boundaries of the flood extent layer and using those elevations to assign floodwater surface elevation to each cell within the flooded domain by identifying its nearest boundary cell. The modeled flood surface elevation is then subtracted from the original DEM to retrieve depth. 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 flood inundation depth. The flood depth map may contain errors due to inaccurate elevations in the NASA SRTM DEM or the water surface estimation. Data Sources: OPERA Dynamic Surface Water eXtent from Harmonized Landsat Sentinel-2 (DSWx-HLS) - based flood inundation extent raster generated by the ARIA/OPERA group at NASA JPL. NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Suggested Use: The darkest shades of blue indicate where the product is estimating the largest floodwater depth, while lighter shades indicate shallower flooding Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites NASA SRTM (Shuttle Radar Topography Mission) Digital Elevation 30 m (openly available on Google Earth Engine) Resolution: 30 meters Credits: Dinuke Munasinghe - NASA JPL Water and Ecosystems Team, NASA JPL ARIA/OPERA Team Product POCs: Dinuke Munasinghe (dinuke.nanayakkara.munasinghe@jpl.nasa.gov) FwDET Algorithm Documentation: Cohen, S., Brakenridge, G.R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y, Munasinghe, D., and J. Zhang (2018). Estimating Floodwater Depths from Flood Inundation Maps and Topography. Journal of the American Water Resources Association, 54(4), 847-858. DOI: https://doi.org/10.1111/1752-1688.12609 Cohen, S., Raney, A., Munasinghe D., Loftis, D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G.R., Kettner, A., Huang, Y., and Y. Tsang (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19(9), 2053-2065. DOI: https://doi.org/10.5194/nhess-19-2053-2019 Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.E., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183. Peter, B., Cohen, S., Lucey, R., Munasinghe, D., Raney, A., and G. Brakenridge (2020). "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis". IEEE Geoscience and Remote Sensing Letters. DOI: https://doi.org/10.1109/lgrs.2020.3031190 OPERA. 2023. OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 CalVal Database (Version 1). Ver. 1.0. PO.DAAC, CA, USA. Dataset accessed [2024-05-09] at https://doi.org/10.5067/OPDSW-PCVV1 Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/2407_opera_fwdet_hls/ImageServer/WMSServer
uav imagery by Pietro S. Fontanella, acquired 2025-12-06 to 2025-12-06, 3.0 cm per pixel, published on OpenAerialMap.
uav imagery by Utaiguara da Nóbrega Borges, acquired 2025-09-16 to 2025-09-16, 2.7 cm per pixel, published on OpenAerialMap.
uav imagery by WMB, acquired 2026-08-21 to 2026-08-21, 25.0 cm per pixel, published on OpenAerialMap.
uav imagery by Bruno Machado Carneiro, acquired 2026-05-26 to 2026-05-27, 5.0 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 3.5 cm per pixel, published on OpenAerialMap.
aircraft imagery by T, acquired 2025-08-30 to 2025-08-30, 2.8 cm per pixel, published on OpenAerialMap.
Date of Image: Pre-Event: March 2024 Post-Event: 5/7/2024, 5/8/2024, 5/9/2024, 5/10/2025, 5/11/2024, 5/12/2024, 5/15/2024 Date of Next Image: Unknown Summary: These Black Marble High-Definition (BMHD) images were created by the NASA Black Marble Science team, with directed funding the NASA-Google Partnership program. The images map the impact of the major flooding in Rio Grande do Sul on electric grids in Porto Alegre. The baseline image (before the floods) was from May 2024, a cloud-free, moon-free composite, and the “after" image was from May 7, 2024. There is a layer to display where clouds are present in the "after" images. This comparison between the two images is meant as a visual assessment of outage impacts from the flooding to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NOTE: Black Marble HD images are downscaled from NASA’s Black Marble nighttime lights product (VNP46), and as such are a “modelled” or “best guess” estimate of how lights are distributed at a 30m resolution. These images should be used for visualization purposes, not for quantitative analysis. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Satellite/Sensor: The primary data source, NASA’s Black Marble nighttime lights product suite (VNP46), utilized to generate this product is derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP) along with high resolution base layers - Landsat derived normalized index products (NDVI and NDWI) and OpenStreetMap (OSM) derived road layer Resolution: Scaled resolution of 30 meters Credits: NASA Black Marble Science team 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 section on right side of page. WMS Endpoint: https://maps.disasters.nasa.gov/ags03/services/brasil_flood_202405/blackmarble_hd/MapServer/WMSServe
uav imagery by T2R.Tech, acquired 2025-09-23 to 2025-09-23, 4.5 cm per pixel, published on OpenAerialMap.
aircraft imagery by ?, acquired 2025-08-19 to 2025-08-19, 4.9 cm per pixel, published on OpenAerialMap.
aircraft imagery by Joao Pessoa, acquired 2025-06-11 to 2025-06-11, 2.7 cm per pixel, published on OpenAerialMap.
uav imagery by Acert Ambiental, acquired 2025-05-07 to 2025-05-07, 4.5 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 2.5 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 2.6 cm per pixel, published on OpenAerialMap.
satellite imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 4.1 cm per pixel, published on OpenAerialMap.
aircraft imagery by PMSMJ, acquired 2026-09-14 to 2026-09-14, 0.9 cm per pixel, published on OpenAerialMap.
Dates of Images: 5/2/2024, 5/4/2024 Date of Next Image: Varies by region, typically 12 days since previous pass. Set time slider to most recent interval and click on area of interest to identify date of last pass. Summary: The Alaska Satellite Facility has developed false color Red, Green, Blue (RGB) and Radiometrically Terrain-Correct (RTC) composites and surface water extent products of the Sentinel-1A/B Synthetic Aperture Radar (SAR) instrument which assigns the co- and cross-polarization information to a channel in the composite. When used to support a flooding event, areas in blue denotes water present at the time of the satellite overpass before or after the start of the flooding event. Sentinel-1 RGB Decomposition of RTC VV and VH imagery over United States coastlines. Blue areas have low returns in VV and VH (smooth surfaces such as calm water, but also frozen/crusted soil or dry sand), Green areas have high returns in VH (volume scatterers such as vegetation or some types of snow/ice), and Red areas have relatively high VV returns and relatively low VH returns (such as urban or sparsely vegetated areas). To identify the date of an image either set the time slider to a 1 day interval or zoom in to AOI, set time slider to desired range and click on the imagery. The name may contain a full date and time in a format like this example: S1A_IW_20210705T231453_DVR_RTC30_G_gpufed_27F4 The date and time would be 07/05/2021 at 23:14:53 UTC. Suggested Use: In this image, water appears in blue, vegetated areas in shades of green and urban areas in bright orange. It is recommended to use this product with ancillary information to derive flooded areas. Satellite/Sensor: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1A/B satellite NOTE: Sentinel-1B is no longer acquiring data and is only available into December 2021 Resolution: 30 meters Esri REST Endpoint: See direct subsetted information on the link the to the right with WMS options. RGB Product: https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_RGB/ImageServer RTC Product: VV - https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_RTC_VV/ImageServer VH - https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_RTC_VH/ImageServer Surface Water Extent Product: https://gis.asf.alaska.edu/arcgis/rest/services/ASF_S1/ASF_S1_WM/ImageServer WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202405_FLOOD_BRAZIL/2407_sentinel1/ImageServer/WMSServer Data Download: Data can be downloaded via the Pop-Ups in the Web Map or through each service's attribute table.
Visão geral Este serviço de feições reúne informações socioeconômicas e territoriais para as Regiões Administrativas do Município do Rio de Janeiro, com foco em indicadores do Índice de Desenvolvimento Social referentes ao ano de 2010. O conteúdo apoia análises espaciais sobre condições de vida, infraestrutura urbana e distribuição territorial do desenvolvimento social. Conteúdo da camada A camada apresenta as Regiões Administrativas como unidades territoriais e agrega indicadores associados a temas como renda, analfabetismo, saneamento, abastecimento de água, coleta de lixo, banheiros domiciliares e características demográficas. Também inclui identificadores administrativos e atributos de referência territorial usados para vincular os registros à estrutura municipal. Usos recomendados Análise da distribuição espacial do desenvolvimento social no Rio de Janeiro. Levantamento de desigualdades territoriais associadas a renda, escolaridade e infraestrutura básica. Apoio a estudos urbanos, planejamento público e monitoramento de políticas sociais. Visualização temática em aplicativos e painéis GIS.
aircraft imagery by jonnathan, acquired 2026-07-25 to 2026-07-25, 1.4 cm per pixel, published on OpenAerialMap.
aircraft imagery by Perito Paulo Rafael, acquired 2026-05-19 to 2026-05-19, 2.7 cm per pixel, published on OpenAerialMap.
aircraft imagery by Filipmanso11, acquired 2025-08-21 to 2025-08-21, 4.8 cm per pixel, published on OpenAerialMap.
uav imagery by Esdras de Lima Andrade, acquired 2025-08-15 to 2025-08-15, 4.2 cm per pixel, published on OpenAerialMap.
uav imagery by Esdras de Lima Andrade, acquired 2025-08-15 to 2025-08-15, 5.0 cm per pixel, published on OpenAerialMap.