Imagery and land cover data
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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.
Established Rural Estate Residential Areas are existing low-density residential communities located within Brampton’s rural area. These areas are intended to maintain their established estate-residential character while recognizing existing development patterns, lot configurations, and servicing constraints. Development and redevelopment within these areas is generally limited and must be compatible with the surrounding rural landscape and community character. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.
Date of Image(s): 12/18/2024; UTC: 183709 1/12/25; UTC: 183731 Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input imagers were generated from the ESA’s Sentinel-2’s Multi-Spectral imager. They were created from the Level 2A products which have been atmospherically corrected and orthorectified. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. For Sentinel 2, NIR = Band 8 and SWIR = Band 12. Using these definitions, the 20-meter resolution Band 8 and Band 12 images were used to generate these products. Sentinel-2 NBR methods: https://custom-scripts.sentinel-hub.com/custom-scripts/sentinel-2/nbr/ Finally dNBR is simply the difference between the pre-fire NBR and the post-fire NBR. Thus, we calculated dNBR as dNBR = NBR(12/18/2024) - NBR(01/12/2025). Suggested Use: NBR is commonly used as a proxy to indicate areas which have charred vegetation. Darker areas (more negative values) in the NBR image more strongly represent the presence of burned vegetation. Since the dNBR considers the condition of the scene before the fire occurred, the resulting value has been used as a proxy for burn severity. More positive values in the dNBR analysis represent a proxy for greater burn severity. Negative values in dNBR represent a re-greening of or growth of vegetation in between pre and post imagery. The use of this dNBR product as a quantitative metric of burn severity of the Los Angeles Fires at the time of posting this dataset should be strongly caveated. This is due to several dNBR Limitations: The spectral band selections used in the NBR and dNBR calculations, and the implication of changes observed following fire in those wavelengths, primarily pertain to how vegetation spectral signatures change in NIR and SWIR wavelengths following charring. Because of this, dNBR may not accurately describe burned surfaces that are not vegetation (e.g. human built infrastructure). This dataset has not been validated by independent burn severity assessments. The degree to which dNBR is accurately determined depends on careful selection of pre and post event imagery. An effort was made to use the highest quality imagery (i.e. cloud free) with the shortest time difference possible in the image pair; however, it is unknown at the time of this posting how selection of different pre/post image pairs could affect the derived dNBR values. Satellite/Sensor: ESA Copernicus Sentinel-2 Resolution: 20 m in infrared bands Credits: Copernicus Sentinel-2 data was downloaded from the Sentinel-2 data explorer (https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2) Data processed by Kyle Kabasares (ARC; kyle.k.kabasares@nasa.gov) References: Normalize Burn Ratio theory: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=3bc872886af24b799acd2ef881691a6a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel2_dnbr/ImageServer/WMSServer
uav imagery by Bruno Machado Carneiro, acquired 2026-05-26 to 2026-05-27, 5.0 cm per pixel, published on OpenAerialMap.
uav imagery by WebODM, acquired 2025-05-07 to 2025-05-07, 5.0 cm per pixel, published on OpenAerialMap.
satellite imagery by Jon T, acquired 2026-06-24 to 2026-06-25, 1.0 cm per pixel, published on OpenAerialMap.
uav imagery by Self, acquired 2025-09-27 to 2025-09-28, 7.0 cm per pixel, published on OpenAerialMap.
satellite imagery by Vantor, acquired 2026-06-29 to 2026-06-29, 43.0 cm per pixel, published on OpenAerialMap.
satellite imagery by Naxa Pvt Ltd, acquired 2026-08-25 to 2026-08-26, 1000 cm per pixel, published on OpenAerialMap.
The Level-1C product is composed of 110x110 km2 tiles (ortho-images in UTM/WGS84 projection). Earth is subdivided on a predefined set of tiles, defined in UTM/WGS84 projection and using a 100 km step. However, each tile has a surface of 110x110 km² in order to provide large overlap with the neighbouring. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic geometry. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances along with the parameters to transform them into radiances.
Mosaico delle Carta Tecnica Regionale scala 1:5.000 di tipo tradizionale su supporto cartaceo realizzata a partire dalla fine degli anni '70 ( Legge Regionale n. 59 del 29/10/1979 ) fino al 1985 con procedimenti di restituzione aerofotogrammetrica, digitallizzata e georeferenziata in coordinate Gauss Boaga fuso est ed ETRF2000 UTM Zone 33N.
This dataset was generated to publish a geospatial inventory of Shopping Centers in Mecklenburg County, NC. This data serves to support and assist Housing & Neighborhood Services.
satellite imagery by Rodolfo Vargas Chile. GEÓGRAFO, acquired 2026-05-20 to 2026-05-20, 100 cm per pixel, published on OpenAerialMap.
satellite imagery by Rodolfo Vargas Chile. GEÓGRAFO, acquired 2026-05-20 to 2026-05-20, 100 cm per pixel, published on OpenAerialMap.
Cartografia Tecnica (CT) storica del Comune di Perugia scala 1:2.000 E.I.R.A. (Ente Italiano Rilevi Aerofotogrammetrici - Anno 1968
satellite imagery by Vantor, acquired 2026-06-26 to 2026-06-26, 32.1 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 NMFRD, acquired 2026-03-25 to 2026-03-25, 1.6 cm per pixel, published on OpenAerialMap.
uav imagery by HUNTERRA, acquired 2025-04-20 to 2025-04-20, 25.5 cm per pixel, published on OpenAerialMap.
uav imagery by Sam Colchester, acquired 2026-04-06 to 2026-04-06, 3.3 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.
uav imagery by Freetown Drone Club, acquired 2025-04-19 to 2025-04-19, 4.0 cm per pixel, published on OpenAerialMap.
satellite imagery by GIS, acquired 2026-07-29 to 2026-07-30, 2.6 cm per pixel, published on OpenAerialMap.
Dates of Images: Post-Event: April 21, 2026 Pre-Event: April 5, 2026 Date of Next Image: Unknown Summary: Natural Color: The Natural Color RGB provides a false composite look at the surface. This RGB uses a shortwave, the near-infrared, and red channels from the instrument. True Color: The True Color RGB composite provides a product of how the surface would look to the naked eye from space. The RGB is created using the red, green, and blue channels of the respective instrument. Suggested Use: Natural Color: areas of water will appear blue, healthy green vegetation will appear as a bright green, urban areas in various shades of magenta. True Color: provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint:
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 OSM, acquired 2026-07-08 to 2026-07-08, 4.8 cm per pixel, published on OpenAerialMap.
uav imagery by T2R.Tech, acquired 2025-09-23 to 2025-09-23, 4.5 cm per pixel, published on OpenAerialMap.
uav imagery by Nikolay Nowitsky, acquired 2025-07-29 to 2025-07-29, 6.5 cm per pixel, published on OpenAerialMap.
satellite imagery by FER, acquired 2025-11-20 to 2025-11-20, 998.9 cm per pixel, published on OpenAerialMap.
uav imagery by WebODM, acquired 2026-01-02 to 2026-01-02, 4.2 cm per pixel, published on OpenAerialMap.
uav imagery by Dronexx, acquired 2025-05-31 to 2025-05-31, 3.0 cm per pixel, published on OpenAerialMap.
uav imagery by Justine Samia, acquired 2025-10-20 to 2025-10-21, 5.1 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.