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 ...

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).

Unclassified three-dimensional point cloud by flightline and classified point cloud by 1 km tile, provided in LAZ format. Classifications follow standard ASPRS definitions. All point coordinates are provided in meters. Horizontal coordinates are referenced in the appropriate UTM zone and the ITRF00 datum. Elevations are referenced to Geoid12A.

The LIDAR point cloud is an archive of hundreds of millions, or sometimes billions of highly accurate 3-dimensional x,y,z points and component attributes produced by the Environment Agency.

The environment agecy site specific LIDAR DSM and DTM Time Stamped Tiles gridded raster products are derived from the point cloud. The component attributes a point cloud contains can provide valuable additional information to supplement elevation and can enable the user to make bespoke raster products such as canopy height models or intensity rasters.

Site specific LIDAR surveys have been carried out across England since 1998, with certain areas, such as the coastal zone, being surveyed multiple times. The point cloud is available for surveys going back to 2006. Although the DSM and DTM Tile Stamped Tiles products are derived from the point cloud data there may not necessarily be a matching point cloud for each surface model due to historic data archiving processes.

During processing the point cloud classifies the laser returns in the 'ground' and 'surface objects'. Further manual editing undertkaen on the derived digital terrain model (DTM) means the classifed ground points in the point cloud data will not match the final derived DTM.

Data is available in 5km download zip files for each year of survey. Within each downloaded zip file are LAZ files aligned to the Ordinance Survey grid. The size of each tile is dependent upon the spatial resolution of the data.

Please refere to the coverage metadata files for the start and end date flown of a survey as well as additional component information the point cloud contains such as the average point density.

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

Semantic segmentation of indoor🏠 point clouds has found various applications in the creation of digital twins for robotics 🤖, navigation 🧭 and building information modeling (BIM) 🏗️. However, most existing datasets of labeled indoor point clouds have been acquired by photogrammetry 📸. In contrast, Terrestrial Laser Scanning (TLS) can acquire dense sub-centimeter point clouds and has become the standard for surveyors.

We present ✨ 3DSES (3D Segmentation of ESGT point clouds) ✨, a new dataset of indoor dense TLS colorized point clouds covering 427 m² of an engineering school. 3DSES has a unique double annotation format: semantic labels annotated at the point level alongside a full 3D CAD model of the building. We introduce a model-to-cloud algorithm for automated labeling of indoor point clouds using an existing 3D CAD model.

3DSES has 3 variants of various semantic and geometrical complexities. We show that our model-to-cloud alignment can produce pseudo-labels on our point clouds with a ⚡ > 95% accuracy ⚡, allowing us to train deep models with significant time savings compared to manual labeling ⌛🚀. First baselines on 3DSES show the difficulties encountered by existing models when segmenting objects relevant to BIM, such as light and safety utilities. We show that segmentation accuracy can be improved by leveraging pseudo-labels and Lidar intensity, an information rarely considered in current datasets. Code and data is released under an open source license.

🚀 Codabench: 3dses_codabench🚀

Github: 3dses_github

This layer contains the Point Cloud for LiDAR data in the Northland region, captured between 18 April 2024 - 28 June 2024.

The DEM is available as layer Northland LiDAR 1m DEM (2024).

The DSM is available as layer Northland LiDAR 1m DSM (2024).

The Index Tiles are available as layer Northland LiDAR Index Tiles (2024).

LiDAR was captured for Regional Software Holdings Ltd by Landpro Ltd from 18 April to 28 June 2024. The dataset was generated by Landpro and their subcontractors. Data management and distribution is by Toitū Te Whenua Land Information New Zealand.

Data comprises:

DEM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

DSM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

Point cloud: las tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

Pulse density specification is at a minimum of 8 pulses/square metre.

Vertical Accuracy Specification is +/- 0.2m (95%) Horizontal Accuracy Specification is +/- 1.0m (95%)

Vertical datum is NZVD2016.

Point-wise annotation was conducted on input point clouds to prepare a labeled dataset for segmenting different sorghum plant-organ. Each sorghum plant's leaf, stem, and panicle were manually labeled in 0, 1, and 2, respectively, using the segment module of the CloudCompare software.

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.

These light detection and ranging (lidar) point clouds (LPCs) were generated from lidar data collected during multiple field campaigns in three study areas near Winter Park, Colorado. Small, uncrewed aircraft systems (sUAS) collected lidar datasets to represent snow-covered and snow-free periods. More information regarding the sUAS used and data collection methods can be found in the Supplemental Information and process step sections of each study area metadata file.

LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3. This dataset, managed by the Office of the Chief Technology Officer (OCTO), through the direction of the District of Columbia GIS program, contains tiled point cloud data for the entire District along with associated metadata.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s LiDAR Point Cloud data product (GLLIDARPC) is to provide high-density individual LiDAR return data, including 3D coordinates, classified ground returns, Above Ground Level (AGL) heights, and LiDAR apparent reflectance. GLLIDARPC data are processed as a LAS Version 1.1 binary format specified by the American Society for Photogrammetry and Remote Sensing (ASPRS). The point cloud includes a density of more than 10 points per square meter. A low resolution browse is also provided showing the LiDAR Point Cloud as an Inverse Data Weighted (IDW) interpolation in PNG format.

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.

This public data repository (https://public.spider.surfsara.nl/project/lidarac/MAMBO/) provides the LiDAR point cloud datasets which were clipped using the boundary polygons (shapefiles) of the MAMBO demonstration sites. The raw LiDAR point cloud tiles were first downloaded from the national repository in the respective country based on the approximate location of each demonstration site. The data repository uses the storage services from the Dutch IT infrastructure SURF (https://www.surf.nl/en). The code for downloading, clipping and uploading the LiDAR point cloud datasets is available on GitHub (https://github.com/Jinhu-Wang/Retile_Clip_LAZ).

This data release consists of three child items distinguishing the following types of data: light detection and ranging (lidar) point clouds (LPCs), digital elevation models (DEMs), and snow depth raster maps. These three data types are all derived from lidar data collected on small, uncrewed aircraft systems (sUAS) at study areas in the Upper Colorado River Basin, Colorado, from 2020 to 2022. These data were collected and generated as part of the U.S. Geological Survey's (USGS) Next Generation Water Observing Systems (NGWOS) Upper Colorado River Basin project.

In support of U.S. Geological Survey (USGS) Southwest Biological Science Center researchers, and in coordination with the Bureau of Land Management (BLM) and National Ecological Observatory Network (NEON), the USGS National Uncrewed Systems Office (NUSO) conducted uncrewed aircraft systems (UAS) remote sensing flights over two BLM Assessment, Inventory, and Monitoring (AIM) plots at the NEON Moab site in Utah for multi-scale carbon sequestration research on public lands. The UAS data collected include natural color, multispectral, and hyperspectral imagery, and lidar to capture diverse information about vegetation and soils on drylands. The first site (“site 1”) features intact sagebrush and was mapped on May 3, 2023. The second site (“site 7”) is located on a grazed rangeland environment and was mapped on May 5, 2023. These UAS surveys were conducted in early May 2023 to coincide spatially and temporally with ground-based BLM AIM sampling and airplane-based remote sensing surveys by NEON. This portion of the data release presents discrete lidar point clouds from low-altitude UAS flights at two dryland sites approximately 40 km south of Moab, Utah. A YellowScan Vx20-100 scanner (laser wavelength 905 nm) was flown at an altitude of 31 meters above ground level on a DJI Matrice 600 Pro UAS with approved government edition firmware. The lidar point clouds were post-processed kinematic (PPK) corrected to a concurrently operating Trimble R8s GNSS base station and each point was assigned Red, Gren, Blue (RGB) image values using corresponding natural color orthomosaics at each site. The point clouds were also point classified using a bare-ground classification scheme (0-Created, never classified; 2-Ground) and exported in .las format.

The dataset includes 6 3D point cloud files collected with Velodyne VLP-16 mobile LiDAR (*.las) belonging to 4 cultural and natural heritage structures located in Cappadocia, Türkiye. The structures are:

1- St. Theodore Church (Interior & Exterior): The Church of St. Theodore is located in Yeşilöz Village in Ürgüp district of Nevşehir. Formerly known as Tagar, now known as Yesiloz Village is approximately 16 km from the center of Urgup district and is a settlement area built on the slope of the valley. The church was carved into a large rock mass on the hill northwest of the village. As a result of excavations near the village, a monastery with a courtyard on three sides was discovered. It is thought that the church belonged to this monastery. The church is called both St. Theodore and Tagar Church. Although it is not known where the name Theodore comes from, it is estimated that this name may have been given because the church was built in the name of St. Theodore.

2- Mustafa Efendi Mosque (Interior & Exterior): The masonry Mustafa Efendi Mosque in Bahçeli Village of Ürgüp District of Nevşehir Province is the oldest of the 3 mosques built in the village. It is estimated that it was built about 50 years before the Osman Efendi Mosque, which was presented as a proposed building within the scope of the project, with a construction date of 1746. Although it is known to have a small inscription with the date of construction, this inscription was not found during the survey. According to this information, Mustafa Efendi Mosque is estimated to be a 17th-18th century work.

3- Fairy Chimney: The distance between Bahçeli Village where the fairy chimney is located and Urgup district is 15 kilometers and the formations between these two areas are generally natural formations without caps and in the late fairy chimney period. It shows that the fairy chimney is a natural formation without a cap and in the late fairy chimney period. The fairy chimney is in the 1st degree natural protected area.

4- Masonry House: Bahçeli Village, where the building examined within the scope of the project is located, is 15 km away from Ürgüp district and is a mixed settlement type. There are approximately 200 cove-carved and masonry historical buildings in the village. A large part of the village, including the structures examined in the village, is a 3rd degree natural protected area. The masonry-rock-carved civil architecture dwelling in Bahçeli Village, Ürgüp District, Nevşehir has not been in use since the 1980s and some of the spaces have been completely lost.

The creation of this dataset was funded by the Scientific and Technological Research Council of Türkiye (TUBITAK) 1001 program under Project no. 122Y017.

Remotely sensed topographic (elevation) and bathymetric (depth) information were acquired for the NSW coast (Point Danger to Cape Howe) and southern Queensland (Palm Beach to Point Danger) using Airborne LiDAR Bathymetry (ALB - a combination of Light Detection And Ranging (LiDAR) and Laser Airborne Depth Sounding (LADS) sensors) during July – December 2018. Data were acquired by Fugro Pty Ltd on behalf of NSW Office of Environment and Heritage using a Riegl VQ-820-G ALB (LiDAR) and Fugro LADS High-Definition sensors aboard sub-contracted Corporate Air Cessna C441 (VH-VEH). Funding was provided through the NSW Coastal Reforms package. The objective of the project was to provide high-resolution data better than 3-5 m spaced soundings (0.5 m spot spacing terrestrial; 3.4 m spot spacing marine) from the mean high-water mark to ~200m inland, and from the shore, seaward (LADS - bathymetry) to the point of laser extinction (~20-40m water depth depending on in-water conditions). Positioning data were collected on the ellipsoid ITRF 2014 GRS80 in UTM Z56 and post-processed using local base stations (CORSnet NSW) to provide a Post Processed Kinematic GNSS solution for final aircraft trajectory before being applied to all data. The data provided here are classified LAS format point clouds, subset into 1) 1 x 1 km tiles of classified height (combined topo-bathymetry) and 2)areas of reflectivity (strength of signal return) data, both in GDA 2020 (horizontal datum; Zones 55 or 56) at Australian Height Datum (vertical datum) with vertical precision to International Hydrographic Order (IHO) 1B. Point cloud data tied to GRS80 ellipsoid is also available. Reflectivity data is further subset into 1) LADS and 2) Riegel sensors. Data covers an area of 6862 km2 and is subdivided into 48 sub-datasets, the extents of which are generally defined in their alongshore extent by the boundaries of NSW Secondary Sediment Compartments (Geosciences Australia). Each data file is prefixed with the compartment name and year of collection. Data provided are available on the ELVIS website (Geosciences Australia - https://elevation.fsdf.org.au). Metadata, data quality statements and geographical data coverage ArcGIS shapefiles are available via SEED https://www.seed.nsw.gov.au/edphome/home.aspx, as are links to the datasets. The data are intended to inform coastal and marine management and should not be used for navigation without additional processing.

This LiDAR point cloud dataset is collected with a research platform of Finnish Geospatial Research Institute (FGI), called Autonomous Research Vehicle Observatory (ARVO). The dataset was collected with Velodyne VLS-128 Alpha Puck LiDAR, 7th of September 2020 in a suburban environment in the area of Käpylä in Helsinki, the capital of Finland. The environment in the dataset consists of a straight two-way asphalt street, called Pohjolankatu, which starts from a larger controlled intersection at the crossing of Tuusulanväylä (60.213326° N, 24.942908° E in WGS84) and passes by three smaller uncontrolled intersections until the crossing of Metsolantie (60.215537° N, 24.950065° E). It is a typical suburban street with tram lines, sidewalks, small buildings, traffic signs, light poles, and cars parked on both sides of the streets. To collect a reference trajectory and to synchronize the LiDAR measurements, we have used a Novatel PwrPak7-E1 GNSS Inertial Navigation System (INS).

The motion distortion of each individual scan has been corrected with a postprocessed GNSS INS trajectory and the scans have been registered with Normal Distributions Transform (NDT). Each point is provided with a semantic label probability vector and the final point cloud is averaged with a 1 cm voxel filter.

The steps to create this preprocessed dataset have been described in more detail in the article "Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity" published in IROS 2022. However, the number of points in each semantic segment in Table I in Section IV-A are different. The correct values are shown in the table below. This does not affect the results.

TABLE I: RandLA-Net classified dataset label proportions.

Semantic label	No. of points	% of all	% of used
Ground	14,206,060	32.3	50.3
Building	7,782,757	17.7	27.6
Tree Trunk	3,736,775	8.5	13.2
Fence	2,201,851	5.0	7.8
Pole	206,983	0.5	0.7
Traffic Sign	85,316	0.2	0.3
Labels used here	28,219,742	64.1	100.0
Others	15,821,962	35.9
Total	44,041,704	100.0

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).