Note: To better find the files to download, select "Change View: Tree". This dataset is associated with the paper "TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds" published in Ecological Informatics and the ML4RS workshop paper "Towards general deep-learning-based tree instance segmentation models" presented at ICLR 2024. It extends the publicly available segmented tree data that was introduced by Calders et al. [1] and Tockner et al. [2]. These two publications only provide segmented trees. For this dataset, these tree labels were propagated to the original point clouds and the remaining points were automatically classified as either "non-tree points" or "unlabeled". Furthermore, some manual correction of the segmented trees was conducted, especially for the tree bases in Tockner et al. [2]. A more comprehensive description of the dataset is given in the linked publications. We provide the laser scans in the original resolution as well as in a voxelized form where the point cloud has been subsampled to contain only one point within a cube with edge length 0.1m. We provide the forest laser scans in the .laz format and follow the same labeling scheme proposed by Puliti et al. [3]. Specifically, a unique identifier is stored as an additional field named "treeID" in the .laz files. Trees are labeled starting from 1 and all non-tree points have the label 0 in the treeID field. The dataset comes with a classification into the three semantic categories "non-tree-points" (label=2), "unlabeled" (label=3) and "tree-points" (label=4) that is saved in the classification field of the .laz file. The .laz format is compatible with popular point cloud processing tools like CloudCompare and can also be loaded in python using the laspy package. Example code for opening .laz files in python as numpy arrays is provided in the open_files.ipynb notebook. References [1] Calders, K., Origo, N., Burt, A., Disney, M., Nightingale, J., Raumonen, P., ... & Lewis, P. (2018). Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sensing, 10(6), 933. [2] Tockner, A., Gollob, C., Kraßnitzer, R., Ritter, T., & Nothdurft, A. (2022). Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS). International Journal of Applied Earth Observation and Geoinformation, 114, 103025. [3] Puliti, S., Pearse, G., Surový, P., Wallace, L., Hollaus, M., Wielgosz, M., & Astrup, R. (2023). FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees. arXiv preprint arXiv:2309.01279.

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 in shapefile format. 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 may vary by project. Some project 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 Documentation

Ontario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX)

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)

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) GTA 2023 - Lift Metadata (SHP)

Ontario Classified Point Cloud (Lidar-Derived) - Tile Index (SHP)

Ontario Lidar Project Extents (SHP)

Data Package Sizes

LEAP 2009 - 22.9 GB

OMAFRA Lidar 2016-18 - Cochrane - 442 GB OMAFRA Lidar 2016-18 - Lake Erie - 1.22 TB OMAFRA Lidar 2016-18 - Peterborough - 443 GB

GTA 2014 - 57.6 GB GTA 2015 - 63.4 GB Brampton 2015 - 5.9 GB Peel 2016 - 49.2 GB Milton 2017 - 15.3 GB Halton 2018 - 73 GB

CLOCA 2018 - 36.2 GB

South Nation 2018-19 - 72.4 GB

York Region-Lake Simcoe Watershed 2019 - 75 GB

Ottawa River 2019-20 - 836 GB

Lake Nipissing 2020 - 700 GB

Ottawa-Gatineau 2019-20 - 551 GB

Hamilton-Niagara 2021 - 660 GB

OMAFRA Lidar 2022 - Lake Huron - 204 GB OMAFRA Lidar 2022 - Lake Simcoe - 154 GB

Belleville 2022 - 1.09 TB

Eastern Ontario 2021-22 - 1.5 TB

Huron Shores 2021 - 35.5 GB

Muskoka 2018 - 72.1 GB Muskoka 2021 - 74.2 GB Muskoka 2023 - 532 GB The Muskoka lidar projects are available in the CGVD2013 or CGVD28 vertical datums. Please specifify which datum is needed when ordering data.

Digital Elevation Data to Support Flood Mapping 2022-26:

Huron-Georgian Bay 2022 - 1.37 TB Huron-Georgian Bay 2023 - 257 GB Huron-Georgian Bay 2023 Bruce - 95.2 GB Kawartha Lakes 2023 - 385 GB Sault Ste Marie 2023-24 - 1.15 TB Sudbury 2023-24 - 741 GB Thunder Bay 2023-24 - 654 GB Timmins 2024 - 318 GB

GTA 2023 - 985 GB

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

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

This dataset contains 11 terrestrial laser scanning (TLS) tree point clouds (in .LAZ format v1.4) of 7 different species, which have been manually labeled into leaf and wood points. The labels are contained in the Classification field (0 = wood, 1 = leaf). The point clouds have additional attributes (Deviation, Reflectance, Amplitude, GpsTime, PointSourceId, NumberOfReturns, ReturnNumber). Before labeling, all point clouds were filtered by Deviation, discarding all points with a Deviation greater than 50. An ASCII file with tree species and tree positions (in ETRS89 / UTM zone 32N; EPSG:25832) is provided, which can be used to normalize and center the point clouds. This dataset is intended to be used for training and validation of algorithms for semantic segmentation (leaf-wood separation) of TLS tree point clouds, as done by Esmorís et al. 2023 (Related Publication). The point clouds are a subset of a larger dataset, which is available on PANGAEA (Weiser et al. 2022b, see Related Dataset). More details on data acquisition and processing, file formats, and quality assessments can be found in the corresponding data description paper (Weiser et al. 2022a, see Related Material).

This deposit includes Bare-earth airborne lidar data collected in 2015, from the south flank of the Santa Ynez Range, north of Montecito, CA, USA. The raw data, collected in 2015 using a Geiger-mode LiDAR scanner, was purchased from Harris Corporation Archive with the Montecito Partners for Resilient Communities fund. Data were subsequently processed using LasTool software. This software was used to clip to the six mountainous watersheds that experienced debris flows during January 2018 and to isolate only classified ground points. The data files series of .laz files encompass an area on the south flank of Santa Ynez Mountains north of Montecito, CA, USA, near 34.4206, -119.6363 in degrees longitude and latitude.

Single photon lidar light detection and ranging (SPL LiDAR) is an active remote sensing technology for: * mapping vegetation aspects including cover, density and height * representing the earth's terrain and elevation contours We acquired SPL data on an airborne acquisition platform under leaf-on conditions to support Forest Resources Inventory (FRI) development. FRI provides: * information to support resource management planning and land use decisions within Ontario’s Managed Zone * information on tree species, density, heights, ages and distribution The SPL data point density ranges from a min of 25pts/m. Each point represents heights of objects such as: * ground level terrain points * heights of vegetation * buildings The lidar was classified according to the Ontario lidar classifications. Low, medium and tall vegetation are classed as 3, 4, 5 and 12 classes. The FRI SPL products include the following digital elevation models: * digital terrain model * canopy height model * digital surface model * intensity model (signal width to return ratio) * forest inventory raster metrics * forest inventory attributes * predicted streams * hydro break lines * block control points Lidar fMVA data supports developing detailed 3D analysis of: * forest inventory * terrain * hydrology * infrastructure * transportation * other mapping applications We made significant investments in Single Photon LiDAR data, now available on the Open Data Catalogue. Derivatives are available for streaming or through download. The map reflects areas with LiDAR data available for download. Zoom in to see data tiles and download options. Select individual tiles to download the data. You can download: * classified point cloud data can also be downloaded via .laz format * derivatives in a compressed .tiff format * Forest Resource Inventory leaf-on LiDAR Tile Index. Download | Shapefile | File Geodatabase | GeoPackage Web raster services You can access the data through our web raster services. For more information and tutorials, read the Ontario Web Raster Services User Guide. If you have questions about how to use the Web raster services, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Note: Internal users replace "https://ws.” with “https://intra.ws." * CHM https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_CHM_SPL/ImageServer * DSM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DSM_SPL/ImageServer * DTM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DTM_SPL/ImageServer * T1 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T1/ImageServer * T2 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T2/ImageServer * Landcover - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer