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.

Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.

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.

Dataset contains 32 terrestrial lidar scans of conifer forests and associated shapefile of locations in Sequoia and Kings Canyon National parks from the summer of 2022. These scans were co-located within field plots from a larger ongoing U.S. Geological Survey (USGS) project collecting wildfire fuels and forest structure data (informally known as the Fire and Fuels Project). These data can also be found in a USGS Earth Resources Observation and Science (EROS) database named IntELiMon (https://dmsdata.cr.usgs.gov/lidar-monitoring/viewer/).

Original Product: These lidar data are processed Classified LAS 1.4 files, formatted to 654 individual 1000 m x 1000 m tiles; used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.

Original Dataset Geographic Extent: 4 counties (Alameda, Marin, San Francisco, San Mateo) in California, covering approximately 53 total square miles.

Original Dataset Descriptio...

All LiDAR overflights by drone were performed in June 2022. The LiDAR sensor used was a Yellowscan Surveyor and was embarked on a DJI Matrice 600 Pro UAV. The YellowScan Surveyor includes: - GNSS-inertial station : Applanix APX-15 UAV - LiDAR : Velodyne VLP16 (also known as Puck) : Wavelength: 905 nm / 300,000 pulses per second (300 kHz) / 2 echoes per pulse / Viewing angle: 360° - Accuracy: 4 cm The design of a flight path consists of a double grid with interline distances of 40 m. The drone flew at 50 m height constrained by a 5 m resolution DTM from the BD ALTI® by IGN. The flights were made at a speed of 5 m per second. All trajectories were planned with the UGCS-4.0.134 software. Then, the LiDAR dataset was post-processed in several steps by using different softwares: - Applanix POSPac UAV 8.4: post-processing (PPK) of trajectories based on the UAV's GNSS-Inertial Measurement System data and using a reference GNSS base station. The correction solution for each trajectory is exported in an ASCII SBET (Smoothed Best Estimated Trajectory) file. - Yellowscan CloudStation V2106.0.0: The SBET file is integrated in the software to generate point clouds in .las format projected in RGF93/Lambert 93. - LidR (R package): reading of las files, redrawing into tiles in compressed format .laz file corresponding to the output format. Telepilots from the TETIS resarch unit, Montpellier : Samuel Alleaume and Florian de Boissieu. This dataset includes raw data and derived raster products. The data is provided in a cloud-optimized format (COPC).

Note: The files can be downloaded from the Attachments section below. Please note that the total size is 180GB, so the download may take some time depending on your system’s capabilities and configuration.

Topographic and bathymetric LiDAR data was collected for New York City in 2017. Topographic data was collected for the entire city, plus an additional 100 meter buffer, using a Leica ALS80 sensor equipped to capture at least 8 pulse/m². Dates of capture for topographic data were between 05/03/2017 and 05/17/2017 during 50% leaf-off conditions. Bathymetric data was collected in select areas of the city (where bathymetric data capture was expected) using a Riegl VQ-880-G sensor equipped to capture approximately 15 pulses/m² (1.5 Secchi depths). Dates of capture for bathymetric were between 07/04/2017 - 07/26/2017. LiDAR data was tidally-coordinated and captured between mean lower low water (+30% of mean tide) ranges.

The horizontal datum for all datasets is NAD83, the vertical datum is NAVD88, Geoid 12B, and the data is projected in New York State Plane - Long Island. Units are in US Survey Feet. To learn more about these datasets, visit the interactive “Understanding the 2017 New York City LiDAR Capture” Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LiDAR_Summary.md

This dataset is a lidar survey by the Middle Usumacinta Archaeological Project. It examines the distribution of archaeological sites in the Middle Usumacinta region in eastern Tabasco, Mexico. Data was collected for Dr. Takeshi Inomata at the University of Arizona.

Publications associated with this dataset can be found at NCALM's Data Tracking Center

The datasets are original and specifically collected for research aimed at reducing registration errors between Camera-LiDAR datasets. Traditional methods often struggle with aligning 2D-3D data from sources that have different coordinate systems and resolutions. Our collection comprises six datasets from two distinct setups, designed to enhance versatility in our approach and improve matching accuracy across both high-feature and low-feature environments.Survey-Grade Terrestrial Dataset:Collection Details: Data was gathered across various scenes on the University of New Brunswick campus, including low-feature walls, high-feature laboratory rooms, and outdoor tree environments.Equipment: LiDAR data was captured using a Trimble TX5 3D Laser Scanner, while optical images were taken with a Canon EOS 5D Mark III DSLR camera.Mobile Mapping System Dataset:Collection Details: This dataset was collected using our custom-built Simultaneous Localization and Multi-Sensor Mapping Robot (SLAMM-BOT) in several indoor mobile scenes to validate our methods.Equipment: Data was acquired using a Velodyne VLP-16 LiDAR scanner and an Arducam IMX477 Mini camera, controlled via a Raspberry Pi board.

The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.

This data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). Processed data, including digital terrain models, snow depth, and canopy height derived from Point Cloud Digital Terrain Models (PCDTMs) are available as SnowEx23 Airborne Lidar-Derived 0.25M Snow Depth and Canopy Height, Version 1.

Product: Processed, classified lidar point cloud data tiles in LAS 1.4 format. Geographic Extent: Approximately 4,028 square miles encompassing the Big Island of Hawaii. Dataset Description: The HI Hawaii Island Lidar NOAA 2017 B17 lidar project called for the planning, acquisition, processing, and production of derivative products of lidar data to be collected at a nominal pulse spacing (NPS...

These are terrestrial laser scanner datasets collected in forested areas west of Flagstaff, Arizona in 2015 and 2016. For each of the two scanners, six treatment areas were scanned, with four of them overlapping one another (Figure 1). These data are composed of individual scans referenced to one another using reflective targets, and geolocated using differentially corrected GPS and RTK locations of scan locations (Figure 3). There were overall large differences in point density among the two scanners (Figure 2).

LiDAR scans for multiple classrooms

. Additionally

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.

This data set is tiled lidar point cloud LAS files v1.4, for the 2016 Alabama 25 County lidar area of interest (AOI).

USGS NGTOC task order G17PD00243 required Spring 2017 LiDAR surveys to be collected over 18,845 square miles covering part or all of 25 counties in Alabama. These counties are Autauga, Baldwin, Barbour, Bullock, Butler, Chambers, Cherokee, Clarke, Conecuh, Covington, Cre...

This data set contains digital terrain models (DTMs) derived from terrestrial lidar scans (TLS) collected as part of the SnowEx 2023 campaign. Data were collected at the Bonanza Creek Experimental Forest near Fairbanks, Alaska in October 2022 (snow-off conditions) and March 2023 (snow-on conditions). The DTMs are provided as Geographic Tagged Image (GeoTIFF) files, where each file corresponds to a unique survey site. Unprocessed point cloud data from which these DTMs were derived are available as the SnowEx23 Bonanza Creek Experimental Forest Terrestrial Lidar Scans Raw, Version 1 (SNEX23_BCEF_TLS_Raw) data set

This data set contains the Doppler wind speed and signal to noise ratio (SNR) from the Cornell University Galion Scanning Doppler Lidar that operated during the Perdigao field season (1 May to 15 June 2017) . The data are in NetCDF format.

LiDAR data for this scene was provided by RIEGAL laser measurement systems from their aerial, mobile and terrestrial LiDAR scanning platforms. A total of 3 LiDAR files were processed and published to the i3S format. Aerial data covering 3.3 Sq Km was flown for downtown Orlando. Which resulted in point cloud of nearly 70 million LiDAR points. A Mobile scan covering 0.8Km of the downtown area outputting a point cloud of 189 Million LiDAR points. A terrestrial LiDAR scan of the buildings on North Roslaind Avenue output a detailed point cloud of the buildings and street, with a point cloud of 19 million LiDAR points. These LiDAR files have been spatially matched and combined in this scene to show the differences in resolution and features captured. Bentley's Context Capture system was used to capture the Integrated mesh of the downtown Orlando area. Consisting of textured mesh of the buildings, foliage and terrain of the downtown area. The imagery for this mesh is courtesy of Track'Air.