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Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT
Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT
Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT
Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT
Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.
Leaf Drop Sites Summary: Leaf Drop Sites Metadata: Leaf Drop Sites.pdf Description: Drop-off sites, located in neighbourhoods across the city, are open seven days a week during daylight hours until Friday, Dec. 4. Leaves are piled loose in these locations for collection. However, any leaves in plastic bags should be de-bagged at the site. Drop-off locations are for leaves only. Please check the individual record's CREATE_DATE for date the feature was added and UPDATE_DATE for the last date an attribute was edited. Find other open data layers on the Kitchener Open Data Repository.
This is a tabular dataset that contains weather information extracted from the Niagara Region South Side Highlift SPS Climate Station. The weather data is a subset of the Niagara RegionWeather Information Systems + Year open data, extracted for 4 hour intervals each day (12 am, 4am, 8am, 12pm, 4pm, 8pm). Weather attributes include: temperature (degrees Celsius), relative humidity (percent), dew point (degrees Celsius), wind speed (km/h), wind gust speed (km/h), wind direction, wind bearing (degrees). Data source:https://niagaraopendata.ca/dataset/niagara-weather-information-systems
This dataset supports monitoring and analysis of passenger overload conditions across the Halifax Transit network. Passenger overload counts are estimated values and are subject to reporting limitations and data variability. Metadata
All recreational programs including: aquatics, clubs, fitness and sport, music, dance, summer camps, preschool and aftercare. Disclaimer: This data is not real-time. The information is updated once a day. For real-time information go to the Online Registration portal (copy and paste link if clicking doesn't take you to page): https://ca.apm.activecommunities.com/activekitchener/Home?_ga=2.33428662.949987444.1519232650-1652255462.1489497433 Fields Description SITE Location of activity CENTRE Building location of activity ENROLLMENT STATUS Status of enrollement: under maximum, under minium, full, full with waiting list SUPERVISOR Supervisor ACTIVITY STATUS Status: Open, Cancelled, Closed ACTIVITY CATEGORY Category ACTIVITY TYPE Activity SEASON Season of registration: Spring, Summer, Fall, Winter TERM Term INSTRUCTOR Instructor name ACTIVITY # Unique number ACTIVITY NAME Activity name ACTIVITY/SUBACTIVITY Activity ACTIVITY START DATE Date the activity starts ACTIVITY END DATE Date the activity ends MIN REGISTRATION Minimum number of registrations MAX REGISTRATION Maximum number of registartions # OF REGISTRATIONS Number of registrants TOTAL HOURS Total Hours of class NUMBER OF SESSIONS Number of sessions WAITS Number of names on wait list HOLDS Number of holds OPEN Number of available openings ACTIVITY OTHER CATEGORY Adult, Child, Teen
Dates of Images: Post-Event: 6/29/2026, 7/01/2026, 7/02/2026, 7/04/2026, 7/6/2026, 7/9/2026, 7/11/2026, 7/14/2026 Pre-Event: 6/16/2026, 6/19/2026 Date of Next Image: Unknown Summary: These pre-fire and post-fire images pair were analyzed for normalized burn ratio (NBR). The input images were generated from the Sentinel-2 dataset at 10m resolution. NBR is defined mathematically as (NIR – SWIR)/(NIR + SWIR) where NIR is near-infrared and SWIR is short-wave infrared. dNBR is computed by the difference between the pre-fire NBR and the post-fire NBR. More information on dNBR can be found here: https://un-spider.org/advisory-support/recommended-practices/recommended-practice-burn-severity/in-detail/normalized-burn-ratio. Check fire containment and image dates for further context on image timing. Federal permitted grazing land maps from the US Forest Service and Department of Interior - Bureau of Land Management (BLM) are shown in the web map. For more information: https://data-usfs.hub.arcgis.com/datasets/usfs::range-unit-allotment-3/about & https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-colorado-grazing-allotments/about 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. Higher dNBR values represent a proxy for greater burn severity. Negative dNBR values may 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 at the time of posting this dataset should be strongly caveated. This is due to several dNBR limitations: The spectral band selections used for 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 representative conditions for each scene; 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: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: True Color: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See layers below. WMS Endpoint: See individual layer pages.
Dates of Images: 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_EX2603_202205_FLOOD_MN/ex2603_severestorm/MapServer/WMSServer
Dates of Images: 20220427T1719, 20220505T1728, 20220507T1719, 20220510T1709, 20220515T1727, 20220522T1718, 20220524T1721, 20220527T1719 (GMT) (YYYYMMDDTHHMM) 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 Harmonized Landsat Sentinel-2 (DSWx-HLS) 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 Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Flood images: April 27, 2022, May 05, 2022, May 07, 2022, May 10, 2022, May 15, 2022, May 22, 2022, May 24, 2022, May 27, 2022 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description:Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Water Gain maps highlight newly inundated areas by comparing pre- and flood-event conditions. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations:We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. 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. The OPERA DSWx-HLS product from 27 April 2022 was likely impacted by unmitigated aerosol effects on the western edge of the image, potentially causing false detections limited to approximately 10 km measured from the western edge into the image. The granule is provided but caution should be taken when interpreting the affected area. Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS Fmask. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_WTR_20220427T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220507T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220510T1709_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220515T1727_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220522T1718_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220524T1721_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220527T1719_mosaic.tif 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a 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) 2: Partial Surface Water - 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: #b4d5f4) 252: Snow/Ice - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 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-HLS water products.. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_20220527T1719_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) Satellite/Sensor/Resolution: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-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 right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_opera_dswx_hls/MapServer/WMSServer
Overview This feature service provides sidewalk information for the Westbank First Nation (WFN) area and supports local inventory and mapping needs for pedestrian infrastructure. Feature Layer Sidewalks: A line feature layer representing sidewalk segments. It includes basic reference information and notes, along with geometry length information used to support spatial management and analysis. Geographic Coverage The service covers the Westbank First Nation (WFN). Use Cases Viewing and maintaining sidewalk locations Supporting pedestrian network and accessibility planning Documenting local infrastructure notes and reference details License and Attribution Owned and maintained by Westbank First Nation (WFN) and made available under the Regional District of Central Okanagan open data license and disclaimer terms.
Dates of Images: 20220427T1719, 20220505T1728, 20220507T1719, 20220510T1709, 20220515T1727, 20220522T1718, 20220524T1721, 20220527T1719 (GMT) (YYYYMMDDTHHMM) 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 Harmonized Landsat Sentinel-2 (DSWx-HLS) 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 Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Flood images: April 27, 2022, May 05, 2022, May 07, 2022, May 10, 2022, May 15, 2022, May 22, 2022, May 24, 2022, May 27, 2022 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description:Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Water Gain maps highlight newly inundated areas by comparing pre- and flood-event conditions. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations:We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. 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. The OPERA DSWx-HLS product from 27 April 2022 was likely impacted by unmitigated aerosol effects on the western edge of the image, potentially causing false detections limited to approximately 10 km measured from the western edge into the image. The granule is provided but caution should be taken when interpreting the affected area. Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS Fmask. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_WTR_20220427T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220507T1719_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220510T1709_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220515T1727_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220522T1718_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220524T1721_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20220527T1719_mosaic.tif 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a 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) 2: Partial Surface Water - 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: #b4d5f4) 252: Snow/Ice - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 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-HLS water products.. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20220505T1728_20220527T1719_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) Satellite/Sensor/Resolution: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-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 right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_opera_dswx_hls/MapServer/WMSServer
Dates of Images Post-Event: 8/3/2026 Summary Skysat is a high-resolution satellite constellation operated by Planet and allows for multiple viewing opportunities of any location on Earth daily. Skysat collects 4-band imagery which allows for multiple derived products to be produced. 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. Resolution ~1 m Credits NASA Disasters Program, Processing from MSFC, Planet. For derived products: “Includes copyrighted material of Planet Labs PBC. All rights reserved.” This work utilized data made available through the NASA Commercial Satellite Data Acquisition (CSDA) program.
Date of Images: 5/24/2022 Date of Next Image: Unknown 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/gis05/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_landsat_WaterExtent/MapServer/WMSServer
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 March 2019 and will retire in December 2029. A replacement item has not been identified at this time. See Sunsetting Legacy Basemaps blog for more information. This map presents land cover and detailed topographic maps for the United States. The map includes the National Park Service (NPS) Natural Earth physical map at 1.24km per pixel for the world at small scales, i-cubed eTOPO 1:250,000-scale maps for the contiguous United States at medium scales, and National Geographic TOPO! 1:100,000 and 1:24,000-scale maps (1:250,000 and 1:63,000 in Alaska) for the United States at large scales. The TOPO! maps are seamless, scanned images of United States Geological Survey (USGS) paper topographic maps. Known Issues: There is a known issue with minor positional shifts (~300 meters) of topo map sheets in the Alaska region. Please be aware if using the topo map for orthorectification or similar purposes.
Layer Overview and Intended UseThe Tropospheric Emissions: Monitoring of Pollution (TEMPO) Cloud Fraction Near Real-Time (NRT) layer provides information on the effective cloud fraction at 466 nm derived from the O2-O2 cloud retrieval algorithm used in TEMPO data processing. The effective cloud fraction is provided on a scale of zero (0) to one (1), with higher values indicating cloudier pixels and is a unitless quantity. The TEMPO cloud product is used to derive vertical columns of TEMPO trace gases (e.g., nitrogen dioxide, formaldehyde). Thus, this cloud fraction layer can be used to identify cloud coverage as observed by the TEMPO instrument, as well as used in conjunction with the TEMPO nitrogen dioxide and formaldehyde NRT image services to provide context and additional information for those data. The TEMPO NRT cloud fraction files are produced using a fast version of the TEMPO baseline retrieval processor. TEMPO NRT cloud fraction Level 3 files provide cloud fraction information on a regular grid. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle, using an area-weighted re-gridding approach. The NRT processing optimizes radiance calibration, image navigation and registration, and cloud and trace gas retrievals for rapid processing, while ensuring that product quality is sufficient for most NRT applications. The data have been converted from their native file format (netCDF4) to Cloud Raster Format (CRF). Temporal CoverageThe temporal resolution of a nominal scan is approximately one hour during daylight hours, with more frequent scans in the morning over the eastern portion of the field of regard and in the evenings over the western portion of the field of regard. Each image is presented with the starting timestamp of the corresponding TEMPO scan. Due to the nature of the TEMPO instrument’s east to west scanning pattern, each image is a composite of measurements taken over a period of 40-60 minutes, depending on the spatial coverage of the scan. Data are updated hourly with any new scans that have been received. Data older than two days (48 hours) are removed from the service. On the rare occasion that the TEMPO instrument is not collecting data for an extended period of time, this service will retain only the latest scan until operations resume. The TEMPO operations log provides details of the instrument’s operations. Geographic CoverageImagery is available for the TEMPO field of regard covering the continental United States (CONUS), and much of Mexico, Canada, and the Caribbean. The imagery in this image service is regridded onto a 0.02º longitude x 0.02º latitude grid and presented in the geographic coordinate system WGS 1984. The sensor’s native spatial resolution is ~2 km x 4.75 km at the center of TEMPO’s field of regard (33.5°N, 89.2°W) in WGS 1984. Data FilteringThe layer is filtered to remove low-quality pixels using the solar zenith angle (removing data retrieved at high solar zenith angles) variable. The applied filter is set to remove pixels based on the following: or solar_zenith_angle > 80. Cloud fraction outside the range between 0.0 and 1.0 represent missing values. Data Validation and Limitations of UseThese data should be considered as provisional products per the Provisional Product Maturity level defined in the TEMPO validation plan. These data are at provisional maturity, which means that product performance has been demonstrated through a large, but still (seasonally or otherwise) limited number of independent measurements. The analysis is sufficient for limited qualitative determinations of product fitness-for-purpose, and the product is potentially ready for testing by operational users and may be suitable for scientific publication. Users may consult the TEMPO User Guide and the TEMPO NRT Data Products User Guide and ATBD Supplement for descriptions of the data and associated known issues. Recommended Usage NotesWhen viewing the image service in the Esri online map viewer, it is recommended to use the multidimensional slider rather than the default time slider. The multidimensional slider can be accessed via the “multidimensional” icon in the right-hand menu. TEMPO Mission OverviewThe Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is a grating spectrometer, sensitive to visible (VIS) and ultraviolet (UV) wavelengths of light with a spectral range of 290-490 + 540-740 nm and 0.6 nm spectral resolution. The TEMPO instrument is attached to the Earth-facing side of a commercial telecommunications satellite (Intelsat 40e) in geostationary orbit over 91˚ W longitude (about 22,000 miles above Earth’s equator). This allows TEMPO to maintain a continuous view of North America so that the instrument's light-collecting mirror can make a complete east-to-west scan of the field of regard hourly during daylight hours. By measuring sunlight reflected and scattered from the Earth's surface and atmosphere back to the instrument's detectors, TEMPO's ultraviolet and visible light sensors provide measurements of ozone, nitrogen dioxide, formaldehyde, and other constituents involved in the chemical and physical processes of Earth’s atmosphere. The primary mission objective of TEMPO is to improve our understanding of air quality and its impacts. By providing near real-time data and comprehensive atmospheric composition measurements, TEMPO helps advance knowledge of the Earth system and improve air quality forecasting. These data also aid in quantifying how air quality affects vegetation, agriculture, and human health, among others. ContactFor inquiries about this service, please contact larc-dl-asdc-tempo@mail.nasa.gov or post/view questions on the Earthdata Forum.
General crash-wide data from the prior 10 years. Data compiled in this format for the Traffic Safety Data and Analysis website (www.iowadot.gov/tsda). Metadata available here.
This feature service provides point locations for manholes within the storm sewer network in the City of Kingston, Ontario. It supports access to stormwater infrastructure records for mapping, inventory management, and municipal operations.
uav imagery by veritas terrae, acquired 2026-05-02 to 2026-05-02, 5.0 cm per pixel, published on OpenAerialMap.
Tacoma 1931 - 3 foot Aerial Photography Contact Info: GIS Team Email: GISteam@cityoftacoma.org Company: Fairchild Aerial Surveys, Inc. Flight Time: May, 1931 Scale: 1” = 800’ Project: C-1540
Parking Public Lots Summary: City-owned Parking Lot Locations Metadata: Parking Public Lots.pdf Description: City-owned Parking Lot Locations Please check the individual record's CREATE_DATE for date the feature was added and UPDATE_DATE for the last date an attribute was edited. Find other open data layers on the Kitchener Open Data Repository.
Overview This service presents a styled hillshade surface for the Regional District of Central Okanagan area. It visualizes shaded relief derived from a digital elevation model using blue and purple symbology, supporting map display and feature queries. Layers Hillshade - bidirectional - multicolor (blue/purple): a grouped hillshade display built from two raster views of the same elevation surface. Hillshade_DEM_RDCO - 360 - #660066: a shaded relief surface rendered in purple tones. Hillshade_DEM_RDCO - 270 - #0059B2: a shaded relief surface rendered in blue tones. Characteristics The service depicts terrain illumination and elevation form as a continuous surface, with each pixel representing hillshade intensity from the underlying elevation model. The layered styling supports visual interpretation of slopes, ridges, valleys, and landform texture. Image interpretation: This service contains shaded relief imagery that represents terrain intensity values across the covered area. It is well suited to cartographic display, terrain visualization, and comparison of relief patterns. Temporal summary: The service is static and presents a single terrain representation with no time-based changes. Geographic Coverage The map coverage spans the Regional District of Central Okanagan area in Web Mercator spatial reference. Use Cases This service supports terrain-rich basemaps, landscape visualization, and contextual mapping for planning, environmental review, and cartographic production.
Overview This feature service provides active transportation facility type information for the district of Peachland. It supports review of active transportation network records and related planning or mapping workflows. Feature Layers Active Transportation - Facility Type: A feature layer describing active transportation facility records with attributes for facility type, priority, and name, supporting identification and organization of transportation assets. Geographic Coverage The service covers the district of Peachland in British Columbia. Use Cases Active transportation planning and inventory management Municipal transportation mapping and reference Review of facility categories and priorities Source and Access Owned by district of Peachland prepared by Urban Systems and distributed under the Regional District of Central Okanagan open data license and terms of use.
Overview This feature service provides zoning information for the County of Brant. It supports review and use of zoning boundaries and associated amendment status within municipal planning workflows. Contents Zoning: Contains zoning area features for the County of Brant, including zoning designation and amendment status. The layer supports reference, planning review, and spatial analysis of zoning coverage across the municipality. Licence This service is shared under CC0.
Emergency Services Summary: Emergency Services Metadata: Emergency Services.pdf Description: Inventory of Emergency Services Please check the individual record's CREATE_DATE for date the feature was added and UPDATE_DATE for the last date an attribute was edited. Find other open data layers on the Kitchener Open Data Repository.
Overview This feature service provides sewer pipe network information for Westbank First Nation (WFN) and supports management of sewer infrastructure within the service area. It includes line features representing sewer pipes and associated asset details used for asset tracking and network reference. Feature Content WFN Sewer Pipes — line features representing sewer pipe assets with identifiers, pipe characteristics, ownership and source information, operational status, location references, and network connectivity attributes. Geographic Coverage The service covers Westbank First Nation (WFN) sewer infrastructure within the Central Okanagan in British Columbia. Use Cases Inventory and reference of sewer pipe assets Network mapping and utility operations Asset management and maintenance planning Support for engineering, infrastructure planning, and field coordination Source and Access Westbank First Nation owns and maintains the data which is hosted by the Regional District of Central Okanagan and is shared under the Regional District of Central Okanagan Open Data License.
The OHN is a provincial medium scale originating from data with regional scales of 1: 10,000 in Southern Ontario, 1: 20,000 in Northern Ontario and 1: 50,000 in the Far North.The shoreline is taken from the OHN – Waterbody data class.This data is used for cartographic purposes and web mapping services. Additional Documentation OHN - User Guide (Word) OHN - Data Capture Specification for Hydrographic Features (Word) Status On going: data is being continually updated Maintenance and Update Frequency Irregular: data is updated in intervals that are uneven in duration Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca