Sec. 368 Corridor Label: Depicts names of designated Section 368 Energy CorridorsSec. 368 Corridor Milepost: This layer depicts milepost point locations along the designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridor centerlines in Bureau of Land Management and U.S. Forest Service Records of Decision in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11 Western States, November 2008. It is intended only as a means to describe locations along the designated corridors. Gaps in the corridor centerlines exist where federal land is not present and there are no designated corridors in these locations, however the gap distances are accounted for in the mileposting, and some mileposts exist in the gaps for continuity in the referencing system.Sec. 368 Designated Corridor - Current: This layer depicts areas which have been designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridors in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008 and the subsequent Records of Decision.Sec. 368 Designated Corridor - Historic: This layer depicts areas which have been Prohibited from Designation or Revised (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridors in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008 and the subsequent Records of Decision.Sec. 368 Designated Corridor Centerline: This layer depicts lines which have been designated (per the requirements of Section 368 of the Energy Policy Act of 2005) as West-wide energy corridor centerlines in Bureau of Land Management and U.S. Forest Service management plans in connection with the final Programmatic Environmental Impact Statement, Designation of Energy Corridors on Federal Land in the 11Western States, November 2008, and the subsequent Records of Decision. Each segment is also attributed with starting and ending mileposts.Regional Review Boundary: Regional review boundaries for Section 368 Energy Corridor reviews.Transmission Line (Wyoming BLM): This feature class contains existing above-ground transmission line geometry across the state of Wyoming. It was digitized from the 2015 NAIP aerial imagery dataset, and was checked for content against the Wyoming Infrastructure Authority data (via NREX) and Platts database data supplied by the BLM National Operations Center. This feature class will continue to be updated on an annual basis in correlation with the BLM's aviation hazards map products revision schedule.Legacy Locally Designated Corridor Area: The dataset consists of locally designated corridors. The dataset was created by combining corridors from multiple BLM sources. Datasets:Existing utility corridors on Kingman Field Office lands (received 9/3/14) Utah corridors (received 9/11/14)Designated BLM utility corridors in Montana (received 9/3/14)Utility corridors as identified by the Resource Management Plan on land managed by the USDOI Bureau of Land Management in the San Luis Valley in SouthCentral Colorado (received 5/14/09)Utility Corridors for the BLM California Desert District (received 7/10/09)Utility corridors in Nevada identified in various land use plans (received 9/3/14) Corridors in Nevada (received 11/3/08)Corridors in the Southern Nevada District Office (received 10/26/16) ROW Corridor designated in Gunnison RMP (received 10/20/2017)Text and map-based descriptions of corridors to remove in Arizona (received 11/8/2017)Legacy Locally Designated Corridor Centerline: This map is designed to display the utility corridors identified in various land use plans. It is a line coverage where lines are assigned labels of existing (some utility in the corridor) corridor, a designated (no utility using the corridor yet) corridor.BLM Solar Energy Zone: This dataset represents Solar Energy Zones available for utility-grade solar energy development under the Bureau of Land Management's Solar Energy Program Western Solar Plan. For details and definitions, see the website at http://blmsolar.anl.gov/sez/.BLM Solar Energy Zone Labels: This feature class was developed to represent Solar Energy Zones as part of the Bureau of Land Management's Solar Energy Program Western Solar Plan.BLM AZ Renewable Energy Dev. Areas: BLM RDEP ROD data. Restoration Design Energy Project Record of Decision, January 2013. This represents the REDA data based upon known resources listed in the ROD Table 2-1, Areas with Known Sensitive Resources (Eliminated from REDA Consideration), known at the time of January 2013. The REDAs may be changed in the future based upon changes in sensitive resources or further analysis and site specific analysis and new baseline data. RDEP decisions are only BLM-administered lands.Bureau of Land Management, Arizona State Office, in conjunction with Environmental Management and Planning Solutions, Incorporated (EMPSi).BLM DRECP Development Focus Area (DFA): This feature class represents Development Focus Areas (DFAs) in the Desert Renewable Energy Conservation Plan (DRECP) Region.BLM DRECP Variance Land: This feature class represents Variance Process Lands in the DRECP.WGA Western Renewable Energy Zone: Depicts renewable energy zone points centered in "geographic areas with at least 1,500 MW of high quality renewable energy within a 100 mile radius", as developed by the Western Governors'Association and U.S. Department of Energy in June 2009. Methodology used to create the dataare described in the WGA report: "Western Renewable Energy Zones - Phase 1Report: Mapping concentrated, high quality resources to meet demand in the WesternInterconnection's distant markets." June 2009.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This raster file represents land within the ESPA classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using U.S. Geological Survey (USGS) Landsat Level 2, Collection 2, Tier 1 data from Landsat 7 and Landsat 8, Sentinel-2 MSI: MultiSpectral Instrument Level-1C data, Mapping Evapotranspiration with Internalized Calibration (METRIC) data (where available) produced by IDWR, USGS National Elevation Dataset (USGS NED) data, Height Above Nearest Drainage (HAND) data, and the U.S. Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. Landsat 7, Landsat 8, and HAND data are at a 30-meter spatial resolution, and the Sentinel-2, USGS NED, and FWS NWI data are at a 10-meter spatial resolution. The Cropland Data Layer (CDL) from the U.S. Department of Agriculture National Agricultural Statistics Service (USDA NASS), Active Water Rights Place of Use (POU) data from IDWR, and National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA) were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. A wetlands mask was applied using FWS NWI data for areas without overlapping irrigation POUs or locations manually determined to have potential irrigation. “Speckling”, or small areas of incorrectly classified pixels, was reduced by using the Boundary Clean smoothing tool in ArcGIS with a descending sorting type. A limited number of manual corrections were also made to improve the accuracy of the results in areas the model struggled with.Due to the large size of the ESPA, imagery had to be processed and input to the Random Forest model in 6 separate “sub-regions” (see Processing Steps). The availability of images varied by sub-region and is outlined for each data source in Processing Steps.