The Rangeland Department in the Kamloops District from the Government of British Columbia has recently raised concerns regarding the observation on the reduction of the number and the surface area of the grassland ponds in the Lac du Bois Grasslands Protected Area. This study aims to distinguish between the ponds with stable groundwater inputs (i.e. connected ponds) and the ponds with unstable groundwater inputs (i.e. perched ponds) to assist the government in determining reliable water sources. This research started by categorizing ponds with different surface areas as either low resilience or threatened resilience. Different terrain models were created using Light Detection and Ranging (LiDAR) data in addition to the calculation of the topographic wetness index (TWI). The classifications were validated using Google Earth and drone imagery. An overall of 121 ponds was discovered with 86 of them considered as low resilience, while the remaining 27 ponds being threatened resilience. For the low resilience ponds, 19 of them were identified as perched ponds, 47 as connected ponds, and 20 as intermediate ponds with the risk of having unstable groundwater connection that requires further analysis in the field. For the threatened resilience ponds, 5 of them were found to be perched ponds, 17 as connected ponds, and 5 as intermediate ponds. The outcome of the pond distribution indicates that the perched ponds were more likely to be found in an area with a flat slope, surrounded by grass, and low canopy coverage. Additionally, the calculated TWI was unable to differentiate between the pond types as the median groundwater levels are spatially dependent on the local topographic features.

The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from LIDAR data taken in the spring between 2007 and 2012. This version contains both ground level samples and items above ground level (such as buildings, bridges, trees etc). The point cloud was converted to a 0.5m …

Data provided in the hillslope gradient dataset relates to hillslope gradient of gullies calculated using two digital elevation models (DEM) of different spatial resolutions as well as the hillslope gradient of gullies collected during a field visit. The dataset that relates to rainfall data suitability includes average and maximum amount of rainfall at selected weather stations and the excavated volumes of gullies in the vicinity of each weather station. Thirdly, the dataset relating to LiDAR, Google Earth, and physical measurements includes the volume of gullies collected during the field visit and gully volume calculated from LiDAR imagery. Furthermore, the dataset includes the aerial extent of gullies measured on Google Earth, the gully aerial extent calculated from the LiDAR imagery, as well as the percentage difference between the measurements. Finally, the fieldwork dataset indicates the coordinates of gullies that were measured in the field, as well as the length, width, and depth measurement results. The dataset also indicates the calculated aerial extent and gully volumes.

City of Edmonton acquired LiDAR data covering the City of Edmonton Corporate Limits plus a 200m buffer. The total project size is 816 km². The LiDAR acquisition was completed between September 4-13, 2019, with a total of 5 flight missions. The purpose of this LiDAR data acquisition was to produce a high-accuracy LiDAR point cloud and several derivative formats such as a 50cm Hydro-flattened Digital Elevation Model (DEM.)

BareEarth.zip is a large file (23.7 GB, SHA256 checksum 524b695ff5a3e6bb67edc47241e3caaa47569f658c25998acad3111c5eb867cb).

If you're having difficulty downloading it from your browser, we have also made it available as a multipart download of 48 parts from Google Drive.

See https://drive.google.com/drive/folders/1ESOkTIiokOANQcElIdUmVoKPyXnjnocI for more information.

THIS ITEM WILL BE UNDERGOING MAINTEANCE SOON TO UPGRADE TO VECTOR TILE BASEMAP LAYERS.Web map displaying Wisconsin DNR-produced Digital Elevation Model (DEM) and Hillshade image services, along with their index layer, in formats that are clickable and can be symbolized and filtered. This map can also be used as a starting point to create a new map. To open the web map from DNR's GIS Open Data Portal, click the View Metadata: link to the right of the description, then click Open in Map Viewer.

The global navigation map market is booming, projected to reach $15 billion by 2025 with a 12% CAGR through 2033. Driven by autonomous vehicles, mobile devices, and enterprise solutions, this report analyzes market trends, key players (Google, TomTom, etc.), and regional growth across North America, Europe, and Asia-Pacific. Discover key insights into this rapidly expanding sector.

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 is a table with metadata for the 3DEP 10m DEM asset. The Work unit Extent Spatial Metadata (WESM) contains current lidar data availability and basic information about lidar projects, including lidar quality level, data acquisition dates, and links to project-level metadata. See more details in this document (taken from this page). Dataset uploaded by Farmers Business Network.

The Digital Elevation Model (DEM) 5 meter Grid of Australia derived from LiDAR model represents a National 5 meter (bare earth) DEM which has been derived from some 236 individual LiDAR surveys between 2001 and 2015 covering an area in excess of 245,000 square kilometers. These surveys cover Australia's populated …

This data set is derived from the original 2005 B4 lidar dataset collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief), "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format. The reprocessing of the 2005 B4 dataset was performed by Dr. Stephen B DeLong, USGS Earthquake Hazards Program, as a service to the community. The data available here were originally published on the USGS ScienceBase website as Classified point cloud and gridded elevation data from the 2005 B4 Lidar Project, southern California, USA. Original B4 project description: The B4 Lidar Project collected lidar point cloud data of the southern San Andreas and San Jacinto Faults in southern California. Data acquisition and processing were performed by the National Center for Airborne Laser Mapping (NCALM) in partnership with the USGS and Ohio State University through funding from the EAR Geophysics program at the National Science Foundation (NSF). Optech International contributed the ALTM3100 laser scanner system. UNAVCO and SCIGN assisted in GPS ground control and continuous high rate GPS data acquisition. A group of volunteers from USGS, UCSD, UCLA, Caltech and private industry, as well as gracious landowners along the fault zones, also made the project possible. If you utilize the B4 data for talks, posters or publications, we ask that you acknowledge the B4 project. The B4 logo can be downloaded here. More information about the B4 Project.

This data set is derived from the original 2005 data collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief) , "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format.

Two 30-m resolution aboveground woody biomass (AGWB) model for the Brazilian Cerrado biome built based on optical satellite imagery (Landsat-5 and Landsat-8) and SAR imagery (ALOS and ALOS-2), using two machine learning algorithms (Random Forest and CART). Field data used to calibrate the models include and a set of plot-based and LiDAR-derived AGWB estimates (n=1,858) from a wide network of researchers in Brazil

Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest surrounding Crested Butte, CO with remotely sensed data, including LiDAR. The flight box design encompassed the watersheds, surveying a total area of 334 km2 across 72 lines. The instrument used was an Optech Gemini, with a pulse density of 2-9 pulses m-2 across the study area (see final report document for detailed information). These LiDAR data are the primary data that were provided by NEON including LAZ files of a classified point cloud, and geotifs of a digital surface elevation model, digital terrain elevation model, and a canopy height model at 1 m resolution with a height threshold of > 2 m. Raster files can also be found on Google Earth Engine: https://code.earthengine.google.com/5c96bbc96ffd50e3c8b1433b34a0bb86.

This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely …

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their structure is poorly mapped due to the use of expensive and time intensive methods. Bathymetric mapping from space has sought to alleviate this but has remained dependent upon in situ water-based measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three common Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for a fully space-borne approach to map nearshore bathymetry. Here we provide the Sentinel-2 mosaics, ICESat-2 bathymetric profiles and Satellite-Derived Bathymetry (SDB) models

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

Discover the booming self-driving 3D high-precision map market! This in-depth analysis reveals a projected $15B market by 2033, driven by autonomous vehicle adoption and technological advancements. Explore market segments, key players (TomTom, Google, Baidu), and regional trends shaping this exciting industry.

Researchers can use this file to compare and contrast a UAV-derived lidar shaded relief map of an archaeological site with the historical VHR satellite imagery accessible via Google Earth. The layers in the.kmz file include: a) an outline showing the surface area depicted in Fig 3; b) outlines of four archaeological structures as interpreted by the authors by analyzing VHR Google Earth imagery from March 2018, April 2019, September 2020, and April 2025; c) bare-earth shaded relief map generated from UAV lidar data of the site. (KMZ)

Terrestrial LiDAR data collected by the team at University College London.

This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices

UCL project name: 2017-07-18.001.riproject

Plot ID: STP

State or region: Camden

Date project started: 7/18/2017

Area scanned: 25,392 m2

Instrument: UCL RIEGL VZ-400

Scan pattern: 19 positions

Angular resolution: 0.04

Images captured: No

Number of scans: 19.0

Google Maps URL: https://www.google.com/maps/place/The+Hardy+Tree/@51.5348275,-0.1302261,19.07z/data=!4m5!3m4!1s0x48761b22ae4a50ff:0x5ee5e6d9819cb888!8m2!3d51.5351276!4d-0.1297699

Publications: https://doi.org/10.1186/s13021-018-0098-0, https://doi.org/10.1016/j.rse.2020.112102

For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017

Please acknowldege the producers of this data set if using this data for publication.

Terrestrial LiDAR data collected by the team at University College London. This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices UCL project name: 2017-02-08.001.riproject Plot ID: RSQ State or region: London Date project started: 2/8/2017 Instrument: UCL RIEGL VZ-400 Scan pattern: Spiral (11 pos) Angular resolution: 0.04 Images captured: Yes Number of scans: 22.0 Google Maps URL: https://www.google.co.uk/maps/place/Russell+Square+Gardens/@51.5216334,-0.1261532,20.51z/data=!4m13!1m7!3m6!1s0x48761b2e1672c317:0x2eb39a3b3d33d9f5!2sMalet+St,+London!3b1!8m2!3d51.5214099!4d-0.1302396!3m4!1s0x48761b313fbfd0a1:0x494a624c4d12c02f!8m2!3d51.5216396!4d-0.1259804 Publications: https://doi.org/10.1186/s13021-018-0098-0 For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017 Please acknowldege the producers of this data set if using this data for publication.