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...

Toronto’s Don River Valley is arguably the city’s most distinctive physical feature. As a provider of water, power, sustenance, building materials, and transportation, it has played an important role in the city’s settlement and development. The river valley has changed dramatically in the years since European settlement, particularly during the late nineteenth and early twentieth century, when the Lower Don River was straightened and channelized and the huge marsh at its mouth drained and filled. Today, the Lower Valley forms the foundation for one of the most densely populated areas in Canada, outlining as it does the eastern portion of Toronto’s downtown core and radiating residential areas. This project documents historical changes in the landscape of the Don River Valley. Drawing from the wide range of geographical information available for the Don River watershed (and the Lower Don in particular), including historical maps, geological maps, fire insurance plans, planning documents, and city directories, the project uses Geographic Information Systems software to place, compile, synthesize and interpret this information and make it more accessible as geospatial data and maps. The project is a work in progress. To date, we have scanned several dozen historical maps of Toronto and the Don River watershed, and compiled the following geospatial datasets: 1) changes to the river channel and shoreline of Toronto harbour, 1858-1918; 2) industrial development in the Lower Don River Watershed, 1857-1951 (as points, and in some cases polygons); 3) historical mill sites in the Don River Watershed, 1825; 18524) land ownership in the watershed, 1860 and 1878; and 4) points of interest in the watershed. In the future, we hope to expand the project to include data from other Toronto area watersheds and other parts of the city. The project was conducted through a collaboration between Jennifer Bonnell, a doctoral student in the History of Education program at the University of Toronto's Ontario Institute for Studies in Education (OISE/UT) - now at York University in the History Department and Marcel Fortin, the Geographic Information Systems (GIS) and Map Librarian at the University of Toronto's Map and Data Library. Financial and in-kind support was provided by the Network in Canadian History and Environment (NiCHE) and the University of Toronto Libraries. Valuable research support for the Points of Interest pages came from Lost Rivers, a community-based urban ecology organization focused on building public awareness of the City's river systems. Jordan Hale, a University of Toronto Geography student conducted much of the digitization and database work.This project could not have been completed without their skilled assistance and dedication.

Zoom in on the map above and click your area of interest to determine which package(s) you require for download.

A three-dimensional raster data set which represents a continuous elevation surface. This data set encompasses the Greater Toronto Area (GTA) and the surrounding area from Niagara to Lake Simcoe and the Kawartha Highlands to Port Hope. The Digital Elevation Model (DEM) data is organized into 20km x 20km tiles with a spatial resolution of 5m.

This data is intended to be used for pre-engineering survey and design as well as the production of planimetric mapping at differing accuracies.

This data is intended for GIS and remote sensing application that require a high resolution, high accuracy elevation model.

The source data for the GTA 2002 DEM can be found in the Ontario Mass Points and Breaklines.

Product Packages

GTA 2002 DEM - North East GTA 2002 DEM - North West GTA 2002 DEM - South West

Additional Documentation

GTA DEM 2002 - User Guide (Word)

GTA 2002 DEM Tile Index (.Zip)

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Not planned: there are no plans to update the data

Contact

Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Data used to make the Disappearing Rivers web map. Includes historical maps, and GIS data for digitized rivers. This data, and the webmap, were developed through a partnership between the Lost Rivers project, and Geohistory-Géohistoire Canada. More information about the web-mapping project is available here.

Contains all of the feature layers created from the Statistics Canada Canadian Proximity Measures database during the webinar series (e.g., the enriched layer, the hot-spot layers, etc.). The Open Database of Healthcare Facilities from Statistics Canada is also provided for context. To view the Web map and turn the layers on and off, click here. For more information about the layers, please refer to the list on the Web map's item page in ArcGIS Online.

Requests must be made to map library staff at above email address and a licence agreement signed. Combines topography and parcel mapping. The series provides a base for thematic mapping services and other published hardcopy products.Depicts the following features:building envelopesbuilding outlinesrailway linesmajor watercoursesmunicipal addressescurbspark names street namesproperty linesright of way boundaries etc.

Field Name - Description/Definition AREA_ID - Internal geographic identifier AREA_NAME - Name of the former municipality OBJECTID - Internal unique object ID This data is a GIS file that outlines visually the geographical administrative boundary of the City of Toronto. This data set is used for creating maps and map applications, as well as for operational use within the City of Toronto. There are two mapping formats available: MTM3 Degree NAD 27 and WGS84 latitude and longitude.

Location data, images, some historical information and maps of Historical Breweries in Toronto. This work and data compilation were inspired by the following book. St. John, Jordan. Lost Breweries of Toronto. Charleston, SC: History Press, 2014. Further Images and maps used in the accompanying Story Maps Workshop can also be found on flickr at https://flic.kr/s/aHsmkrMS9g and https://flic.kr/s/aHskDv5WiU

Index grid for Toronto Property Data Maps (PDM), 2019.

This series combines topography and parcel mapping, and provides a base for thematic mapping services and other published hardcopy products. Depicts the following features: building envelopes, building outlines, railway lines, major watercourses, municipal addresses, curbs, park names, street names, property lines, right of way, boundaries, etc.

Field Name - Description/Definition AREA_ID - Internal geographic identifier AREA_NAME - Name of the former municipality OBJECTID - Internal unique object ID This data is a GIS file that outlines visually the geographical administrative boundary of the City of Toronto. This data set is used for creating maps and map applications, as well as for operational use within the City of Toronto. There are two mapping formats available: MTM3 Degree NAD 27 and WGS84 latitude and longitude.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This dataset is derived from the 2003 PDF map by the City of Toronto depicting building construction dates by parcels. The 2021 Toronto Parcels were used to attach the built date. Please Note: This is not an official City of Toronto dataset and should be used for reference, teaching and consultation purposes only and not for analysis

Green Spaces is a geospatial dataset of polygons that is a point-in-time generalized representation of existing parks and open spaces within the City of Toronto. Green Spaces includes public parks, beaches, parts of ravines, golf courses, cemeteries, and other open space areas such as those offered by various private institutions. These spaces may or may not be publicly accessible and are either publicly owned/maintained or they are privately owned/maintained. Green Spaces is not an exhaustive dataset of all the public and private parks and open spaces within the City. It does not encompass all areas protected by the Ravine and Natural Feature Protection By-Law, by Environmentally Significant Areas or by Natural Heritage Areas. Green Spaces is primarily intended to visualize our existing overall parks and open space system. It is used to symbolize the green coloured areas in many of the City's mapping products such as Toronto's Interactive Map or the Toronto Parkland Strategy[^1]. This dataset is jointly maintained by the Geospatial Competency Centre (GCC) and the Parks, Forestry & Recreation division (PFR). The attribute field in this dataset titled "AREA_CLASS" indicates whether the GCC or PFR maintain the data and the type of green space it is. PFR Green Spaces PFR Green Spaces are owned and/or operated by the City of Toronto and are derived from PFR's Parkland Asset Repository. They include the area classes: Park, Golf Course, Cemetery, Hydro Field/Utility Corridor, and others. Representation of Green Spaces can change overtime – due to new park acquisitions, development/reconfiguration of land etc. – PFR works to continuously improve the City-PFR-owned portions of this dataset as new information arises. GCC Green Spaces All other Green Spaces were created by the GCC and include area classes with the prefix of "OTHER_". These green spaces are derived through city-wide parcel analysis. [^1]This Green Spaces layer cannot be used to reproduce the results of the 2019 Parkland Strategy, however the GCC Green Spaces portions of the dataset was used to help visualize Other Open Spaces in "Figure 06: Toronto’s Parks and Open Space Network" of the report. The Toronto Parkland Strategy is currently in the process of being updated and new parkland data is not yet finalized.

Use the Geospatial Ontario Imagery Orders App to select and request uncompressed or compressed digital imagery tiles from Geospatial Ontario for transfer directly to you.

This colour aerial photography was flown using a Zeiss Jena LMK15 aerial camera equipped with a 6" (152mm) focal length lens mounted in a Piper Aztec F aircraft. It was captured on September 15, 28, and 29, 1995. The altitude of the aircraft, 15,000 feet above mean ground, provided photography at a nominal scale of 1:30000. Originally captured on film, the imagery was converted digitally and orthorectified. The final product has the same accuracy as a 1:20000 map.

Time of Capture: September 1995 Coverage: 6,289 sq km Canopy Coverage: Leaf-Off

Available Products Orthorectified Tiles - approx. 5km x 2.8km, 100cm resolution, 8-bit, RGB in .TIFF format (40MB/tile) and a compressed format (approx. 1MB/tile)

Greater Toronto Area (GTA) 1995 - Ortho Index (Shapefile)

Additional ResourcesImagery User Guide (.docx) Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Not planned: There are no plans to update the data

Contact

Ontario Ministry of Natural Resources - Geospatial Services, imagery@ontario.ca

This map is currently under development.The Transportation Tomorrow Survey (TTS) is a comprehensive travel survey conducted in the Greater Golden Horseshoe Area (GGHA) once every five years. Participants in the design and implementation of the survey include Cities of Toronto and Hamilton, the Regional Municipalities of Durham, Halton, Peel and York, GO Transit, the Toronto Transit Commission and the Ontario Ministry of Transportation.The survey collects information about households, demographics, and trips in an extremely detailed manner. In order to provide some summary data to the public, the University of Toronto's Data Management Group provides ward-level (Greater Toronto Hamilton Area only) and planning-district level counts (entire GGHA) for over 400 different categories. The list of downloadable open data tables can be found on the group's website. This map contains a number of layers which show ward-level counts for the Greater Toronto Hamilton Area data for a categorized subset of the 400+ categories available including:Household sizeNumber of trips to and from individual wards by mode in a 24 hour period.Number of trips to and from individual wards by purpose in a 24 hour period. *Not yet available*Fields with a value of 4 or less have been rounded to zero to preserve anonymity.

This dataset includes all Motor Vehicle Collision (MVC) occurrences by their occurrence date and related offences. The MVC categories include property damage (PD) collisions, Fail to Remain (FTR) collisions, injury collisions and fatalities. This data is provided at the occurrence level, therefore multiple offences and/or victims can be associated with each record. Traffic Collisions DashboardDownload DocumentationIn this dataset, a collision is defined as the contact resulting from the motion of a motor vehicle or streetcar or its load, which produces property damage, injury or death. The term collision indicates that the initial point of contact involved at least one motor vehicle or streetcar.Definitions:Fatal Collisions occur when an individual’s injuries from a MVC result in a fatality within 30 days. Please note this category excludes:(i) Occurrences on private property(ii) Occurrences related to sudden death prior to collision (suicide or medical episode)(iii) Occurrences where the individual has died more than 30 days after the collisionPersonal Injury Collisions occur when an individual involved in a MVC suffers personal injuries. Fail to Remain Collisions occur when an individual involved in a MVC fails to stop and provide their information at the scene of a collision.Property Damage Collisions occur when an individual’s property has been damaged in a MVC or the value of damages is less than $2,000 for all involved parties.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Extreme heat is the most common climate-related hazard globally, with rising temperatures and more frequent heat waves affecting cities, ecosystems, and food production. Urban heat islands (UHIs), where city temperatures are higher than surrounding rural areas, are becoming more prevalent due to climate change. This occurs because urban structures like buildings and roads trap more heat than natural landscapes. To address this, creating a heat risk index (HRI) is essential for developing localized adaptation plans and prioritizing areas most at risk. This web map showing health risk index (HRI), temperature variations, population density, tree canopy cover across Toronto city. The inputs for this HRI was derived from multiple data sources from the ArcGIS Living Atlas of the World.

The City of Toronto’s SolarTO Map was created through a Geographic Information Systems (GIS) analysis of Light Detection and Ranging (LiDAR) data. The software takes into account geographical latitude, as well as the sun’s daily position throughout the year. When users input their address, the Map generates an estimate of solar photovoltaic (PV) system size that can fit on the roof, its cost, savings and payback period in addition to greenhouse gas emission reduction calculations.

Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download.The DSM data is available in the form of 1-km by 1-km non-overlapping tiles grouped into packages for download.This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters and sensors may vary by project. See the detailed User Guide linked below for additional information.You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Now also available through a web service which exposes the data for visualization, geoprocessing and limited download. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connectionusing the REST endpoint URL. The service draws using the Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServerhttps://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer (Government of Ontario Internal Users)Additional DocumentationOntario DSM (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-2018 - Cochrane - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Peterborough - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Lake Erie - Additional Contractor Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Contractor Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Contractor Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Contractor Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Contractor Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Contractor Metadata (Word)Timmins Lidar 2024 - Additional Contractor Metadata (Word)Cataraqui Lidar 2024 - Additional Metadata (Word)Chapleau Lidar 2024 - Additional Metadata (Word)Dryden-Ignace-Sioux Lookout Lidar 2024 - Additional Metadata (Word)Atikokan Lidar 2024 - Additional Metadata (Word) Ontario DSM (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP)Product PackagesDownload links for the Ontario DSM (Lidar-Derived) (Word)Projects:LEAP 2009GTA 2014-18OMAFRA 2016-18CLOCA 2018South Nation CA 2018-19Muskoka 2018-23York-Lake Simcoe 2019Ottawa River 2019-20Ottawa-Gatineau 2019-20Lake Nipissing 2020Hamilton-Niagara 2021Huron Shores 2021Eastern Ontario 2021-22OMAFRA Lake Huron 2022OMAFRA Lake Simcoe 2022Belleville 2022Digital Elevation Data to Support Flood Mapping 2022-26Huron-Georgian Bay 2022-23Kawartha Lakes 2023Sault Ste Marie 2023-24Sudbury 2023-24Thunder Bay 2023-24Timmins 2024Cataraqui 2024Chapleau 2024Dryden 2024Ignace 2024Northeastern Ontario 2024Sioux Lookout 2024Atikokan 2024Greater Toronto Area Lidar 2023StatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download.The DTM data is available in the form of 1-km by 1-km non-overlapping tiles grouped into packages for download.This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters and sensors may vary by project. See the detailed User Guide linked below for additional information.You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Now also available through a web service which exposes the data for visualization, geoprocessing and limited download. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connection using the REST endpoint URL. The service draws using the Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca.Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DTM_LidarDerived/ImageServerhttps://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DTM_LidarDerived/ImageServer (Government of Ontario Internal Users)Additional Documentation Ontario DTM (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-2018 - Cochrane - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Peterborough - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Lake Erie - Additional Contractor Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Contractor Metadata (PDF)Huron-Georgian Lidar 2022-23 - Additional Contractor Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Contractor Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Contractor Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Contractor Metadata (Word)Timmins Lidar 2024 - Additional Contractor Metadata (Word)Cataraqui Lidar 2024 - Additional Metadata (Word)Chapleau Lidar 2024 - Additional Metadata (Word)Dryden-Ignace-Sioux Lookout Lidar 2024 - Additional Metadata (Word)Atikokan Lidar 2024 - Additional Metadata (Word) Ontario DTM (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP) OMAFRA Lidar DTM 2016-2018 - Cochrane - Breaklines (SHP)OMAFRA Lidar DTM 2016-2018 - Peterborough - Breaklines (SHP)OMAFRA Lidar DTM 2016-2018 - Lake Erie - Breaklines (SHP)CLOCA Lidar DTM 2018 - Breaklines (SHP)South Nation Lidar DTM 2018-19 - Breaklines (SHP)Ottawa-Gatineau Lidar DTM 2019-20 - Breaklines (SHP)OMAFRA Lidar DTM 2022 - Lake Huron - Breaklines (SHP)OMAFRA Lidar DTM 2022 - Lake Simcoe - Breaklines (SHP)Eastern Ontario Lidar DTM 2021-22 - Breaklines (SHP)Muskoka Lidar DTM 2018 - Breaklines CGVD2013 (SHP) / CGVD28 (SHP)Muskoka Lidar DTM 2021 - Breaklines CGVD2013 (SHP) / CGVD28 (SHP)Muskoka Lidar DTM 2023 - Breaklines CGVD2013 (SHP) / CGVD28 (SHP)DEDSFM Huron-Georgian Bay 2022-23 - Breaklines (SHP)DEDSFM Kawartha Lakes 2023 - Breaklines (SHP)DEDSFM Sault Ste Marie 2023-24- UTM16 - Breaklines (SHP)DEDSFM Sault Ste Marie 2023-24- UTM17 - Breaklines (SHP)DEDSFM Sudbury 2023-24 - Breaklines (SHP)DEDSFM Thunder Bay 2023-24 - Breaklines (SHP)DEDSFM Timmins 2024 - Breaklines (SHP)DEDSFM Cataraqui 2024 - Breaklines (SHP)DEDSFM Chapleau 2024 - Breaklines (SHP)DEDSFM Dryden 2024 - Breaklines (SHP)DEDSFM Ignace 2024 - Breaklines (SHP)DEDSFM Sioux Lookout 2024 - Breaklines (SHP)DEDSFM Northeastern Ontario 2024 - Breaklines (SHP)DEDSFM Atikokan 2024 - Breaklines (SHP)Product PackagesDownload links for the Ontario DTM (Lidar-Derived) (Word)Projects:LEAP 2009GTA 2014-18OMAFRA 2016-18CLOCA 2018South Nation CA 2018-19Muskoka 2018-23York-Lake Simcoe 2019Ottawa River 2019-20Ottawa-Gatineau 2019-20Lake Nipissing 2020Hamilton-Niagara 2021Huron Shores 2021Eastern Ontario 2021-22OMAFRA Lake Huron 2022OMAFRA Lake Simcoe 2022Belleville 2022Digital Elevation Data to Support Flood Mapping 2022-26Huron-Georgian Bay 2022-23Kawartha Lakes 2023Sault Ste Marie 2023-24Sudbury 2023-24Thunder Bay 2023-24Timmins 2024Cataraqui 2024Chapleau 2024Dryden 2024Ignace 2024Northeastern Ontario 2024Sioux Lookout 2024Atikokan 2024Greater Toronto Area Lidar 2023StatusOn going: Data is continually being updatedMaintenance and Update FrequencyAs needed: Data is updated as deemed necessaryContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca