Canada GIS data

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of April across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of May across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of October across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of November across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

A local area plan is created by The City in coordination with participants including residents and landowners, local businesses, community associations and builders/developers. Generally, local area plans are created over approximately 2.5 – 3 years with input collected through an iterative engagement process. A local area plan supports neighbourhoods experiencing redevelopment by outlining: An overarching vision and core values to guide the evolution of the area over the next 30 years. A concept for where and how new development can be integrated into the neighbourhood over time (if/when development is proposed). Development policies to help realize great development in the area. Future local investment options to support growth and change.

This feature layer contains Lennox and Addington Lot's and Concession information. This layer provides a comprehensive spatial representation of land parcels and concessions.

Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of March across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download

Overview This feature service provides stormwater asset point locations maintained by WFN and served through RDCO infrastructure. It supports viewing, reviewing, and managing storm drainage assets within the service area. Feature Layer WFN Storm Points: A point feature layer representing stormwater infrastructure assets. The layer includes asset identification,  installation details, orientation, and maintenance-related comments, supporting operational stormwater inventory and asset management. Geographic Coverage The service covers the Westbank First Nation (WFN) area within the Regional District of Central Okanagan. Use Cases Stormwater asset inventory and field reference Infrastructure maintenance and condition tracking Operational planning for drainage networks Supporting utility GIS workflows Source and Licensing The service is created and maintained by Westbank First Nation (WFN) and is hosted on RDCO servers. It is shared under the Regional District of Central Okanagan Open Data License, with the accompanying disclaimer and terms of use.

Elevation Points - Section I, last updated in 2026. Part of A through R, divided by concession blocks across the municipal extents.  Often used for 3D modelling.  UTM Co-ordinates, with height Z-values in Metres.

Overview This feature service provides a point inventory of water appurtenances maintained by Westbank First Nation (WFN). It supports operational tracking and asset management for water system components across the service area. Feature Content Water points representing appurtenances such as air release valves, blow offs, caps, curb stops, manholes, reducers, and valves. Supporting asset details for each point, including jurisdiction, condition, operation and maintenance rating, installation date, orientation, and asset identification. Geographic Coverage The service covers Westbank First Nation (WFN) managed water infrastructure within the Regional District of Central Okanagan area and related jurisdictions served by the network. Use Cases Water utility asset inventory and maintenance planning Operational mapping of valves and other appurtenances Condition assessment and lifecycle tracking Supporting field crews, inspections, and infrastructure updates Source and Use Created and maintained by Westbank First Nation (WFN) and hosted on RDCO servers, this service is available under the Regional District of Central Okanagan open data license and terms of use.

This dataset is a compilation of address point data from Dakota County address authorities, which are cities and townships. The dataset is intended to contain a point location and the official address (as defined by the address authority) for all occupiable units and any other official addresses within the jurisdictional boundary of each address authority. A number of other attributes are available in the dataset, but may not be populated by some address authorities. Dakota County is playing a coordinative role to work with cities and townships to create, maintain and aggregate address points.   Esri File Geodatabase DownloadEsri Shapefile Download

Dates of Images: Post-Event: 12/17/2025 Pre-Event: 10/13/2025 Date of Next Image: Unknown Summary: 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. Cloud mask indicates areas marked as obfuscated by clouds. Suggested Use: The True Color RGB 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. Cloud mask indicates areas marked as obfuscated by clouds. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI), Landsat 8; MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 Meters (Sentinel-2); 30 Meters (Landsat) 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_202512_FLOOD_WA/2512_sentinel2_cloud/ImageServer/WMSServer

uav imagery by NYenvironcom, acquired 2026-07-08 to 2026-07-08, 4.1 cm per pixel, published on OpenAerialMap.

uav imagery by Veteran Droners, acquired 2026-02-17 to 2026-02-18, 2.0 cm per pixel, published on OpenAerialMap.

uav imagery by NYenvironcom, acquired 2026-07-08 to 2026-07-09, 3.0 cm per pixel, published on OpenAerialMap.

Retirement Notice: This item is in mature support as of February 2026 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. This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey.  This layer is symbolized to show the count and percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Retirement Notice: This item is in mature support as of February 2026 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. This layer shows workers' place of residence by mode of commute. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.   This layer is symbolized by the count of total workers and the percentage of workers who drove alone. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B08301 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.   Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.  NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

uav imagery by WebODM, acquired 2025-05-03 to 2025-05-03, 4.2 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM-LowestPotential, acquired 2025-05-10 to 2025-05-10, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM-LowestPotential, acquired 2025-05-12 to 2025-05-12, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Parks Canada, acquired 2025-05-15 to 2025-05-15, 4.4 cm per pixel, published on OpenAerialMap.

uav imagery by TCRCG, acquired 2025-04-18 to 2025-04-18, 0.5 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-05-20 to 2025-05-20, 7.8 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-05-25 to 2025-05-25, 2.0 cm per pixel, published on OpenAerialMap.

uav imagery by UNBC, acquired 2025-05-30 to 2025-05-30, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Hawkinson Aerial Photography, LLC, acquired 2025-06-02 to 2025-06-02, 2.7 cm per pixel, published on OpenAerialMap.

uav imagery by Faine Greenwood, acquired 2025-06-19 to 2025-06-19, 2.8 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-06-27 to 2025-06-27, 6.8 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-06-27 to 2025-06-27, 6.6 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM-LowestPotential, acquired 2025-07-25 to 2025-07-25, 4.7 cm per pixel, published on OpenAerialMap.

uav imagery by Sean Frey, acquired 2025-08-03 to 2025-08-03, 3.6 cm per pixel, published on OpenAerialMap.

uav imagery by WebODM, acquired 2025-08-08 to 2025-08-08, 6.5 cm per pixel, published on OpenAerialMap.

uav imagery by Sean Frey, acquired 2025-08-12 to 2025-08-12, 3.4 cm per pixel, published on OpenAerialMap.

uav imagery by Hawkinson Aerial Photography, LLC, acquired 2025-08-19 to 2025-08-19, 3.7 cm per pixel, published on OpenAerialMap.

uav imagery by Hawkinson Aerial Photography, LLC, acquired 2025-08-19 to 2025-08-19, 3.7 cm per pixel, published on OpenAerialMap.

uav imagery by Sean Frey, acquired 2025-09-03 to 2025-09-03, 3.1 cm per pixel, published on OpenAerialMap.