This Datasets contains the Kitti Object Detection Benchmark, created by Andreas Geiger, Philip Lenz and Raquel Urtasun in the Proceedings of 2012 CVPR ," Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite". This Kernel contains the object detection part of their different Datasets published for Autonomous Driving. It contains a set of images with their bounding box labels and velodyne point clouds. For more information visit the Website they published the data on (http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d).

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface.

Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.

The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE.

Virginia LiDARThe Virginia LiDAR Inventory Web Mapping Application provides access to LiDAR point cloud and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. Data is obtained from NOAA and USGS data portals. LiDAR Point Clouds are compressed for file storage and transfer. Informational Access Type:1) LiDAR Project Metadata: To download individual LiDAR project Metadata, click on a LiDAR inventory polygon for link to the host FTP site. Once at the host site, locate appropriate directory and .zip file to receive project documentation and accompanying project files. For use within ArcGIS, the geospatial grid and inventory data powering the VGIN LiDAR download inventory services can be downloaded under conversion and analysis resources below.2) LiDAR Point Clouds (Single): To download individual tiles, zoom in on the map until the tile grid appears. The VGIN Composite Geocoding service is available to use when querying by physical address, feature, or community anchor institution name. Click a tile to identify grid information for individual LiDAR Point clouds. Columns note where the LiDAR is hosted and what format is available for download. In many instances, multiple results are returned due to multiple file formats and flight years. If LiDAR data is missing spatial reference information please refer to the metadata in step 1 above. Tile grids are stacked so you will need to scroll through selections:3) LiDAR Point Clouds (Bulk): To download multiple files in a single FTP directory folder, which can be a necessity in many instances, consider the use of a multi-file download manager plugin to use with your browser in conjunction with the URLs provided on the LiDAR inventory polygon. If LiDAR data is missing spatial reference information please refer to the metadata in step 1 above. For use within ArcGIS, the geospatial grid and inventory data powering the VGIN LiDAR Download Inventory Services can be downloaded under conversion and resources below.Conversion and Resources:Convert to LAS from USGS/NOAA hosted .LAZ filesDownload LiDAR Inventory Data Project FootprintsDownload LiDAR Inventory Tile GridContact:For questions about the data please contact USGS For questions about the application please contact vbmp@vdem.virginia.gov

This data was collected by the Geological Survey Ireland, the Department of Culture, Heritage and the Gaeltacht, the Discovery Programme, the Heritage Council, Transport Infrastructure Ireland, New York University, the Office of Public Works and Westmeath County Council. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows: GSI – 1m DCHG/DP/HC - 0.13m, 0.14m, 1m NY – 1m TII – 2m OPW – 2m WMCC - 0.25m Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The grid cell size varies depending on the organisation that collected it. GSI data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared. .hidden { display: none }

The i.c.sens Visual-Inertial-LiDAR Dataset is a data set for the evaluation of dead reckoning or SLAM approaches in the context of mobile robotics. It consists of street-level monocular RGB camera images, a front-facing 180° point cloud, angular velocities, accelerations and an accurate ground truth trajectory. In total, we provide around 77 GB of data resulting from a 15 minutes drive, which is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the intrinsic camera parameters and the extrinsic transformations between all sensor coordinate systems are given. Details on the data and its usage can be found in the provided documentation file.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/0ff90ef9-fa61-4ee3-b69e-eb6461abc57b/download/sensor_platform_small.jpg" alt="">

Image credit: Sören Vogel

The data set was acquired in the context of the measurement campaign described in Schoen2018. Here, a vehicle, which can be seen below, was equipped with a self-developed sensor platform and a commercially available Riegl VMX-250 Mobile Mapping System. This Mobile Mapping System consists of two laser scanners, a camera system and a localization unit containing a highly accurate GNSS/IMU system.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/2a1226b8-8821-4c46-b411-7d63491963ed/download/vehicle_small.jpg" alt="">

Image credit: Sören Vogel

The data acquisition took place in May 2019 during a sunny day in the Nordstadt of Hannover (coordinates: 52.388598, 9.716389). The route we took can be seen below. This route was completed three times in total, which amounts to a total driving time of 15 minutes.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/8a570408-c392-4bd7-9c1e-26964f552d6c/download/google_earth_overview_small.png" alt="">

The self-developed sensor platform consists of several sensors. This dataset provides data from the following sensors:

• Velodyne HDL-64 LiDAR
• LORD MicroStrain 3DM-GQ4-45 GNSS aided IMU
• Pointgrey GS3-U3-23S6C-C RGB camera

To inspect the data, first start a rosmaster and launch rviz using the provided configuration file:

roscore & rosrun rviz rviz -d icsens_data.rviz

Afterwards, start playing a rosbag with

rosbag play icsens-visual-inertial-lidar-dataset-{number}.bag --clock

Below we provide some exemplary images and their corresponding point clouds.

https://data.uni-hannover.de/dataset/0bcea595-0786-44f6-a9e2-c26a779a004b/resource/dc1563c0-9b5f-4c84-b432-711916cb204c/download/combined_examples_small.jpg" alt="">

Related publications:

• R. Voges, C. S. Wieghardt, and B. Wagner, “Finding Timestamp Offsets for a Multi-Sensor System Using Sensor Observations,” Photogrammetric Engineering & Remote Sensing, vol. 84, no. 6, pp. 357–366, 2018.

• R. Voges and B. Wagner, “RGB-Laser Odometry Under Interval Uncertainty for Guaranteed Localization,” in Book of Abstracts of the 11th Summer Workshop on Interval Methods (SWIM 2018), Rostock, Germany, Jul. 2018.

• R. Voges and B. Wagner, “Timestamp Offset Calibration for an IMU-Camera System Under Interval Uncertainty,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 2018.

• R. Voges and B. Wagner, “Extrinsic Calibration Between a 3D Laser Scanner and a Camera Under Interval Uncertainty,” in Book of Abstracts of the 12th Summer Workshop on Interval Methods (SWIM 2019), Palaiseau, France, Jul. 2019.

• R. Voges, B. Wagner, and V. Kreinovich, “Efficient Algorithms for Synchronizing Localization Sensors Under Interval Uncertainty,” Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 1–11, 2020.

• R. Voges, B. Wagner, and V. Kreinovich, “Odometry under Interval Uncertainty: Towards Optimal Algorithms, with Potential Application to Self-Driving Cars and Mobile Robots,” Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 12–20, 2020.

• R. Voges and B. Wagner, “Set-Membership Extrinsic Calibration of a 3D LiDAR and a Camera,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, Oct. 2020, accepted.

• R. Voges, “Bounded-Error Visual-LiDAR Odometry on Mobile Robots Under Consideration of Spatiotemporal Uncertainties,” PhD thesis, Gottfried Wilhelm Leibniz Universität, 2020.

This data collection of the 3D Elevation Program (3DEP) consists of Lidar Point Cloud (LPC) projects as provided to the USGS. These point cloud files contain all the original lidar points collected, with the original spatial reference and units preserved. These data may have been used as the source of updates to the 1/3-arcsecond, 1-arcsecond, and 2-arcsecond seamless 3DEP Digital Elevation Models (DEMs). The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Lidar (Light detection and ranging) discrete-return point cloud data are available in LAZ format. The LAZ format is a lossless compressed version of the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. Point Cloud data can be converted from LAZ to LAS or LAS to LAZ without the loss of any information. Either format stores 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of geo-referenced x, y coordinates and z (elevation), as well as other attributes for each point. Additonal information about the las file format can be found here: https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities. All 3DEP products are public domain.

Lidar point cloud data with classifications – unclassified (1), ground (2), low vegetation (3), medium vegetation (4), high vegetation (5), buildings (6), low point - noise (7), reserved – model keypoint (8), high noise (18).

The Environment Agency National LIDAR Programme provides accurate elevation data at 1m spatial resolution for all of England.

In 2017 we divided the country into 302 survey blocks covering all of England which were subsequently captured during the winter months (approximately November to April each year) between January 2017 and February 2023. These are known as our 'Phase 1' national lidar programme surveys.

Subsequently we have undertaken repeat surveys of specific blocks based on the on-going requirements for upto date elevation data. Each repeat survey block is given a new incrementing phase number, for example the second time we capture a block this is that blocks 'phase 2' whilst the 3rd time will be 'phase 3'. There is not curretly a plan to capture all the origianl phase 1 survey blocks over a rolling programme with repeat surveys be based on the requirements for upto date elevation data for an area.

All data is published through the DEFRA Data Services survey portal on a quartely on-going bases and a number of different products area available for each survey block. All products are available in 5km tiles aligned to the ordnance survey grid. The tiles are named by the unique survey id, OS grid reference and the first and last survey date of the survey id (P_XXXXX_OSOSOS_SDFLOWN_EDFLOWN.*). The surface models are available in GeoTiff raster format whilst the point cloud is available in *.laz. An index catalogue is also available with provides survey specific information about each tile.

Outlined below is a description of each product that is available for each survey block:

LIDAR Point Cloud: is the discrete LIDAR returns that are used in the creation of the surface models. Supplied in *.laz format they the discrete LIDAR returns have been classified into ground, low, medium and high vegetation classes using an automated classification process.

Digital Surface Model(s) (DSM) are created from the last or only LIDAR pulse returned to the sensor and contains all ground and surface objects.

Digital Terrain Model(s) (DTM) is created from the last return LIDAR pulse classified as ground, filtering out surface objects. Manual filtering is undertaken on the DTM to improve the automated classification routines to produce a most likely ground surface model. Areas of no data, such as water bodies, are also filled to ensure there are no gaps in the model.

First Return Digital Surface Model(s) (FZ DSM) is created from the either the first or only LIDAR pulse returned to the sensor and contains all ground and surface objects. It is more likely to return elevations from the top or near top of trees and the edges of buildings. It can often be used in canopy height modelling and production of building outlines.

Intensity Surface Model(s) (Int DSM) is a measure of the amount of laser light from each laser pulse reflecting from an object. This reflectivity is a function of the near infrared wavelength used and varies with the composition of the surface object reflecting the return and angle of incidence.The intensity surface model produces a grayscale image where darker surfaces such as roads reflect less light than other surfaces such as vegetation.

These lidar data are processed classified LAS 1.4 files at USGS QL2 covering the District of Columbia. Voids exist in the data due to data redaction conducted under the guidance of the United States Secret Service. This dataset provided as an ArcGIS Image service. Please note, the download feature for this image service in Open Data DC provides a compressed PNG, JPEG or TIFF. The individual LAS point cloud datasets are available under additional options when viewing downloads.

LiDAR elevation data that can be downloaded by selections of tiles using individual, box graphic, polygon graphic, or by GIS polygon features.

Constraints:
Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The point cloud is structured into non-overlapping 1 km by 1 km tiles in LAZ format. The following classification codes are applied to the data: * unclassified * ground * water * high noise * low noise This dataset is a compilation of lidar data from multiple acquisition projects, so specifications, parameters, accuracy and sensors may vary by project. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis. Related data: Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived).

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return.Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features.Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data was collected by several organisations. All raster data formats are provided as GeoTIFF rasters. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The data is available in different resolutions. For example some data has a grid cell size of 2 meter by 2 meter. This means that each cell (pixel) represents an area of 2 meter squared.This viewer provides access to download processed LiDAR data in raster format.This data was collected by the Geological Survey Ireland (GSI), the Department of Culture, Heritage and the Gaeltacht (DCHG), the Discovery Programme (DP), the Heritage Council (HC), Transport Infrastructure Ireland (TII), New York University (NYU), the Office of Public Works (OPW) and Westmeath County Council (WMCC). All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows:GSI – 1mDCHG/DP/HC - 0.13m, 0.14m, 1mNY – 1mTII – 2mOPW – 2mWMCC - 0.25m

This zip file contains LIDAR, Digital Terrain Models (DTM's), surface, and breakline datasets covering the extent of Peoria County. The LIDAR data was captured during spring leaf-off in 2008. There are eight databases in Esri's file geodatabase format which are broken down by eight areas in the County. The DTM's conform to the ASPRS Class I Standards using the Illinois State Plane West coordinate system. Please contact us if you would like a copy of the data.More recent LIDAR data for Peoria County, IL was captured in 2012 by the State of Illinois through the Illinois Height Modernization Program (ILHMP). Please click Here to read about the program and data available for download.Contact InformationPeoria County GISEmail: gis@peoriacounty.orgPhone: 309-495-4840This data is bound to the Peoria County GIS Open Data License Agreement which can be found here: https://data-peoriacountygis.opendata.arcgis.com/pages/peoria-county-gis-open-data-license-agreement.

This web map allows for the download of KyFromAbove LiDAR data by 5k tile in LAZ format. This point cloud data was acquired during the typical leaf-off acquisition period (winter-spring) over a period of several years and may be provided as LAS version 1.1, 1.2, or 1.4 depending upon the acquisition period. Users will need to download the LAZIP.exe in order to decompress each tile. LiDAR data specifications adopted by the KyFromAbove Technical Advisory Committee can be found here. This is the source data used to create the Commonwealth's 5 foot digital elevation model (DEM) and its associated derivatives. More information regarding this data resource can be found on the KyGeoPortal.

Download 2016, 2020 and 2024 LiDAR Data Exchange Files (LAS) and derived products (DEM, DEM Intensity, DSM, and DSM Intensity) by tile using an index. On May 16, 2024, Aero-graphics acquired high resolution LiDAR data over approximately 9 square miles located in the Aspen area. LAS files, comprised of classified LiDAR based on contract specifications, were created then broken into Public Land Survey (PLS) tiles for data download.

This .las file contains sample LiDAR point cloud data collected by National Ecological Observatory Network's Airborne Observation Platform. The .las file format is a commonly used file format to store LIDAR point cloud data.This teaching data set is used for several tutorials on the NEON website (neonscience.org). The dataset is for educational purposes, data for research purposes can be obtained from the NEON Data Portal (data.neonscience.org).

Virginia LiDAR

The Virginia LiDAR Inventory Web Mapping Application provides access to LiDAR point cloud, bare earth digital terrain elevation models (DEM's) and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. The application links to resources served from NOAA and USGS data portals.

This high resolution LiDAR dataset of the village of Nuiqsut is part of a series of 2013 LiDAR and imagery collections by EPSCoR Northern Test Case. This LiDAR was collected at 8 points per meter (ppm) over the village of Nuiqsut as part of a broader area coverage to go with the high density collection over the Northern Test Case focus area of Crea Creek (link below) The data products contain a 1 meter Digital Elevation Model (DEM) and raw LAS files. For data download access contact support@gina.alaska.edu. In the future this data will also be available for download at: http://maps.dggs.alaska.gov/elevationdata

Scope of LIDAR data for which the DDT des Vosges is responsible for the Vosges department (source DDT88/SER/BPR).

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link. See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index. Data sizes by project area are listed below. The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format. This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors vary by project. Some projects have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders. Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived). You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Additional DocumentationOntario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX) Ontario Classified Point Cloud (Lidar-Derived) - Tile IndexOntario Lidar Project Extents (SHP) OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Metadata (Word)Timmins Lidar 2024 - Additional 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) OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP)CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP)South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP)York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP)Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP)Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP)DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP)DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP)DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Cataraqui Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Chapleau Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Dryden Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Ignace Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Sioux Lookout Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Northeastern Ontario Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Atikokan Lidar Point Cloud 2024 - Lift Metadata (SHP)GTA 2023 - Lift Metadata (SHP) Data Package SizesLEAP 2009 - 22.9 GBOMAFRA Lidar 2016-18 - Cochrane - 442 GBOMAFRA Lidar 2016-18 - Lake Erie - 1.22 TBOMAFRA Lidar 2016-18 - Peterborough - 443 GBGTA 2014 - 57.6 GBGTA 2015 - 63.4 GBBrampton 2015 - 5.9 GBPeel 2016 - 49.2 GBMilton 2017 - 15.3 GBHalton 2018 - 73 GBCLOCA 2018 - 36.2 GBSouth Nation 2018-19 - 72.4 GBYork Region-Lake Simcoe Watershed 2019 - 75 GBOttawa River 2019-20 - 836 GBLake Nipissing 2020 - 700 GBOttawa-Gatineau 2019-20 - 551 GBHamilton-Niagara 2021 - 660 GBOMAFRA Lidar 2022 - Lake Huron - 204 GBOMAFRA Lidar 2022 - Lake Simcoe - 154 GBBelleville 2022 - 1.09 TBEastern Ontario 2021-22 - 1.5 TBHuron Shores 2021 - 35.5 GBMuskoka 2018 - 72.1 GBMuskoka 2021 - 74.2 GBMuskoka 2023 - 532 GBDigital Elevation Data to Support Flood Mapping 2022-26:Huron-Georgian Bay 2022 - 1.37 TBHuron-Georgian Bay 2023 - 257 GBHuron-Georgian Bay 2023 Bruce - 95.2 GBKawartha Lakes 2023 - 385 GBSault Ste Marie 2023-24 - 1.15 TBSudbury 2023-24 - 741 GBThunder Bay 2023-24 - 654 GBTimmins 2024 - 318 GBCataraqui 2024 - 50.5 GBChapleau 2024 - 127 GBDryden 2024 - 187 GBIgnace 2024 - 10.7 GBNortheastern Ontario 2024 - 82.3 GBSioux Lookout 2024 - 112 GBAtikokan 2024 - 64 GBGTA 2023 - 985 GB StatusOn 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