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.

This dataset is Lidar data that has been collected by the Scottish public sector and made available under the Open Government Licence. The data are available as point cloud (LAS format or in LAZ compressed format), along with the derived Digital Terrain Model (DTM) and Digital Surface Model (DSM) products as Cloud optimized GeoTIFFs (COG) or standard GeoTIFF. The dataset contains multiple subsets of data which were each commissioned and flown in response to different organisational requirements. The details of each can be found at https://remotesensingdata.gov.scot/data#/list

This layer contains the New Zealand national 1m DEM which is an amalgamation of the most current LiDAR surveys. It is current as at the most recent Published date found in the History tab.

For information on datasets that make up the New Zealand 1m DEM, please visit this layer: New Zealand 1m DEM Survey Index

The most recent DEM data can also be accessed from the NZ Elevation public S3 bucket via the link below, which is automatically updated when new elevation layers are added.

Data management and distribution is by Toitū Te Whenua Land Information New Zealand. Management of this layer is ongoing and it will be updated with newer data as it becomes available.

Data comprises: • 400+ GeoTIFF tiles in NZTM2000 projection, merged from a variety of source surveys and tiled into the LINZ 1:50,000 tile layout.

Vertical Accuracy Specification is +/- 0.2m (95%). Horizontal Accuracy Specification is +/- 1.0m (95%). Vertical datum is NZVD2016.

Also available on: • Basemaps as a Terrain-RGB service.

• NZ Elevation - Registry of Open Data on AWS, specifically at s3://nz-elevation/new-zealand/new-zealand/dem_1m/2193/. This is the data source and at this location it is LERC compressed and made available as Cloud Optimised GeoTIFFs.

Additional metadata from the S3 source: • STAC Collection

• Capture Dates

• Capture Area

Please note that this dataset is manually imported into the LINZ Data Service after the S3 source is updated. It may not reflect the most recently published data in S3. Check the History tab to determine the most recent LINZ Data Service publish date. S3 source update dates are provided in the STAC Collection metadata file.

Use with the following attribution: "Sourced from the LINZ Data Service and licensed by Toitū Te Whenua Land Information New Zealand, for re-use under CC BY 4.0".

For details see: https://www.linz.govt.nz/data/licensing-and-using-data/attributing-elevation-or-aerial-imagery-data"

Accepts and provides access to high-resolution (meter to sub-meter scale) Earth science-oriented topography data (e.g. LiDAR) and bathymetric data, and related tools and resources. The OpenTopography Tool Registry provides a community populated clearinghouse of software, utilities, and tools oriented towards high-resolution topography data (e.g. collected with LiDAR technology) handling, processing, and analysis. Tools registered range from source code to full-featured software applications. Contributions to the registry via the Contribute a Tool page are welcome. OpenTopography also hosts a dataset catalog to which users can register datasets hosted elsewhere; these entries are discoverable by users alongside OpenTopography hosted datasets. Lidar point cloud data are available in LAS, LAZ and ASCII formats. Raster datasets and derived products can be downloaded in Arc ASCII, IMG, and GeoTIFF formats. Derived products and visualizations are available in Google Earth KML format. The OpenTopography user community and advisory committee provides feedback to define the scope of collaborations on data hosting and cyberinfrastructure development

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. This DSM is derived from an edited DSM named WorldDEM, where flattening of water bodies and consistent flow of rivers has been included. In addition, editing of shore- and coastlines, special features such as airports, and implausible terrain structures has also been applied.

The WorldDEM product is based on the radar satellite data acquired during the TanDEM-X Mission, which is funded by a Public Private Partnership between the German State, represented by the German Aerospace Centre (DLR) and Airbus Defence and Space. OpenTopography is providing access to the global GLO-90 Defence Gridded Elevation Data (DGED) 2023_1 version of the data hosted by ESA via the PRISM service. Details on the Copernicus DSM can be found on this ESA site.

• Previous to July 23rd 2024, OpenTopography was providing access to the Copernicus data through the public AWS S3 bucket established by Sinergise. As of July 23rd 2024, Opentopography is providing the DGED 2023_1 version of GLO-90 as downloaded directly from ESA
• The original gridded data from ESA is in geographic coordinates where the longitudinal cell spacing increases as a function of latitude for regions north of 50N and south of 50S. For more details see the Grid Spacing section of the Copernicus DEM handbook. In order to keep the pixel dimensions uniform, OpenTopography resamples data north of 50 degrees latitude and south of -50 degrees latitude in order to output a consistent 3 Arc-second product for data accessed through the web-interface or API. Users who need data north of 50N or south of 50S, and prefer to use the original, longitude-varying grid spacing can download cloud optimized geotiff (COG) versions of the tiles from our bulk download interface, or download the original data directly from ESA.
• The GLO-90 datasets are available on a free basis for the general public under the terms and conditions of the Copernicus license found here.

The LiDAR Point Clouds is a product that is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an open government license and has been incorporated into the National Elevation Data Strategy. Point cloud files are distributed by LiDAR acquisition project without integration between projects. The point cloud files are distributed using the compressed .LAZ / Cloud Optimized Point Cloud (COPC) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment. The reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).

Le produit Nuages de points lidar fait partie de la Série CanÉlévation créée pour appuyer la Stratégie nationale de données d’élévation mise en oeuvre par Ressources naturelles Canada (RNCan). Ce produit contient les nuages de points obtenus lors de divers projets d’acquisition par lidar aéroporté réalisés au Canada. Ces projets d’acquisition par lidar aéroporté peuvent avoir été réalisés par RNCan ou par divers partenaires. Les données de nuages de points lidar ont une licence de type gouvernement ouvert et ont été intégrés à la Stratégie nationale de données d’élévation. Les fichiers de nuages de points sont distribués par projet d'acquisition et sans intégration entre les projets. Les fichiers de nuages de points sont distribués en format compressé .LAZ / Cloud Optimized Point Cloud (COPC). Le format ouvert COPC est une réorganisation en octree des données à l’intérieur même d’un fichier .LAZ 1.4. Il permet une utilisation et un rendu de visualisation efficace via des appels HTTP (ex : via le web), tout en offrant les capacités propres au format .LAZ compressé qui est déjà bien établi dans l’industrie. Les fichiers de nuages de points sont donc autant téléchargeables pour une utilisation locale que visualisables via des liens URL provenant d’un environnement infonuagique. Le système de référence utilisé pour tous les nuages de points du produit est le NAD83(SCRS), époque 2010. La projection utilisée est la projection UTM avec le fuseau correspondant. Les élévations sont orthométriques et exprimées par rapport au Système canadien de référence altimétrique de 2013 (CGVD2013).

As part of the research presented in the paper: “LiDAR and Photogrammetry Data Evaluation from a Lava Tube Mission in Sicily”, conducted within the ESA co-funded Rhizome 2.0 project, this dataset documents the processing of the mapping data of Grotta di Monte Intraleo, a terrestrial lava tube on Mt. Etna, Sicily, serving as an analogue site for extraterrestrial exploration. The data available in this repository builds on previously published data, which is also available unprocessed, and supports comparative evaluations of LiDAR and photogrammetric approaches, informing habitat design strategies for the underground environments of Martian and Lunar lava tubes.

The dataset comprises three primary components:

1. Final LiDAR Point Clouds: This file contains the complete registered and merged point cloud of Grotta di Monte Intraleo, alongside its processing history. Subfolders include the five Unregistered Parts (individual scans), Registered Parts (aligned using CloudCompare’s ICP Registration), and the Merged and Final Cloud. Additionally, the file includes the navigation mesh produced by the quadruped robot during autonomous traversal.
2. Final Photogrammetric Point Cloud: This file contains the photogrammetric reconstruction of the lava tube, derived from two separate video footages and processed with the software Agisoft Metashape and CloudCompare. It includes both the Unmerged registered scans and the Merged final cloud.
3. Cloud-to-Cloud Distance Analyses: This file provides quantitative comparisons of the datasets. It includes (a) distance analyses between LiDAR and photogrammetric point clouds (additional to the published paper), and (b) analyses of alignment error between individual LiDAR scans (as discussed in the paper).

Data processing was performed using the Open Source software CloudCompare, which employed ICP registration, Cloud-to-Cloud distance metrics, and Poisson surface reconstruction. Users can explore and visualize the datasets by installing CloudCompare and loading the .bin files.

This dataset has been curated to enable reproducible research in robotic mapping, autonomous navigation, and 3D reconstruction of subsurface environments. Beyond terrestrial applications, it contributes to the ongoing evaluation of lava tubes as candidates for sustainable extraterrestrial habitats.

The National Bathymetric Source (NBS) project creates and maintains high-resolution bathymetry composed of the best available data. This project enables the creation of next-generation nautical charts while also providing support for modeling, industry, science, regulation, and public curiosity. Primary sources of bathymetry include NOAA and U.S. Army Corps of Engineers hydrographic surveys and topographic bathymetric (topo-bathy) lidar (light detection and ranging) data. Data submitted through the NOAA Office of Coast Survey’s external source data process are also included, with gaps in deep water filled through Global Multi-Resolution Topography, a merged model of bathymetry. Different vertical datums and file formats are made available to meet various uses. The BlueTopo folder includes multilayer floating point GeoTIFFs with associated Raster Attribute Tables (RAT) containing elevation, vertical uncertainty, with other quality metrics and source information. These files are arranged in a spatial tiling and resolution scheme corresponding to the Electronic Navigational Chart (ENC) but are not for navigation due to the inclusion of additional non-navigation data and non-navigation vertical datums. For navigational datasets please see the S-102 distribution portal. "nowCOAST" provides public access to BlueTopo through the nowCOAST viewer, web map tile services (WMTS), and links to individual datasets.

We presented the background information for the study “An assessment of the N load from animal farms in saline wetland catchments in the Ebro Basin, NE Spain”. The studied wetlands basins are the: Saladas de Alcañiz-Calanda, Gallocanta Lake, Saladas de Sástago-Bujaraloz and Sariñena Lake. All the areas are catalogued in the Natura 2000 European Network, under the European Directives Birds (2009/147/EC) and Habitats (92/43/EEC), and are nitrates vulnerable areas according to the European Water Framework Directive (WFD, 91/676/EC) designation. The data presented here belong to the research projects AGROWET, funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”, and ISABEL, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. The dataset includes 4 compressed folders: “Saladas de Alcañiz-Calanda (ALC).zip”, “Gallocanta Lake (GA).zip”, “Saladas de Sastago-Bujaraloz (BU).zip” and “Sariñena Lake (SA).zip”. Each folder comprises fives GIS files generated in ArcGIS v.10.8: the “Watershed_name_of_basin”, “dem_name_of_basin”, “NVZ_name_of_basin” (Nitrate Vulnerable Zone), “Livestock_name_of_basin” and “Crop_name_of_basin”. “Watershed_name_of_basin” is a shape (polygon) delimited following the drainage divides with the help of the digital elevation model (DEM). “DEM_name_of_basin” is a raster file generated from the LiDAR data with a vertical accuracy of 2 m and a density of 0.5 points m2. “NVZ_name_of_basin” is a shape (polygon) downloaded from the National Spatial Data Infrastructure of the MITECO website (https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua/zonas-vulnerables.html). “Livestock_name_of_basin” is a shape (point) of the livestock census collected as Table from the official administrative Register of Livestock Farms of Aragon, Spain (REGA), and it was available in the Open Data platform (https://opendata.aragon.es/). This shape presents a legend layer (.lyr). “Crop_name_of_basin” is a shape (polygon) based on the information provided by the farmer declarations in the context of the Common Agricultural Policy (CAP). This shape presents a legend layer with the declared crops grouped for a better spatial representation. The abovementioned geographic layers are map projected in the European Datum ETRS89/UTM zone 30N. The dataset includes an Excel spreadsheet “shape_attribute_info.xlsx” where the head columns of the attribute table of the shapes “livestock” and “crop” are explained. Apart from the basic information from the whole basins, we include the compressed folder named “Farnaca.zip” containing the geographic files used for the specific study of this area in the “Saladas de Sástago-Bujaraloz”: the shape (polygon) delimitating the study area (“area_farnaca”), and the slope of this polygon (“slope_farnaca”) with their corresponding legend layer.

Note for access: The data is available to anyone interested, but in order to monitor access, we ask that interested users request access by logging in by using the account of their academic institution, selecting the files they want, and clicking "Request Access" If you do not have access through your institution, please contact us by clicking "Contact Owner", enter your email address, and mention the list of files you need. In both cases, please include a note stating your institution and your purpose for using the data. This dataset contains the point cloud files, the resulting airfoil B-splines, connectors, surface meshes, and surface CAD files for the three blades presented in the paper "Transforming Laser-Scanned 750 kW Turbine Surface Geometry Data into Smooth CAD for CFD Simulations," which correspond to the three blades of the WINSENT test site. In this dataset, the blades are named according to the numbers identified on the tip of each physical blade: 008, 022, and 025, corresponding to blades A, B, and C, respectively, in the paper. Note: each blade was processed independently by the script, and as such, there will be small differences at the root. This means that the match between the blade root and the hub attachment point should be adjusted individually for each blade prior to simulation. Also, note that the blades are aligned along the z-axis, and the tower was thus tilted equivalently to the shaft axis; it is likely necessary to untilt the whole turbine prior to simulation. Additionally, note that the 0-pitch angle does not match the angle in the x-y plane in the given blade geometries. To have the blade in the almost (should be better than +/- 0.5°) proper 0° pitch position, you should rotate the blade by -7.5° around the z-axis. The explanations for the individual files are provided in the accompanying file descriptions. Supplementary information is also given here for convenience. The most accurate reconstructions of the blades are those with the closed trailing edge. These reconstructions closely adhere to the flat and slightly rounded trailing edge observed on the physical blades and from the scan data. The FreeCAD files include surfaces generated from 193 original 3D-smoothed B-splines created by the splprep package in Python, as part of the automated reconstruction, interpolation, and smoothing program described in the paper. Both the FreeCAD and .iges CAD files are recognized as the official blade geometries for the WINSENT wind turbines. The differences between the closed and open trailing edge versions of the provided blade sufaces are localized to the actual trailing edge and do not perceptibly influence the trailing edge thickness. To facilitate the use of the open trailing edge CAD data, a surface being only the trailing edge itself is also provided. For those interested in CFD or generating meshes of the turbine blades, the Plot3D structured and STL surface meshes of the blade (with the last 0.2% of the blade radius cut off and left as a hole) generated directly from the splprep B-splines from the developed software are available. Alternatively, the same data is also provided as a series of Pointwise connectors. Both the Plot3D and connectors are in ASCII format, whereas STL is in binary format. For those wanting to vary the level of smoothing used in the 3D-smoothing step, the B-splines prior to 3D-smoothing are also provided in Python's Pickle format. There are two source cloud datasets given: A) The original clouds from the laser scanning campaign, with most surrounding artifacts removed but without any modifications to the point data; these clouds are in ASCII format (.txt), compressed as bz2 tar archives. The first three columns provide x, y, z coordinates, and the fourth column the intensity of the laser reflection, which is necessary for realigning the scans using the targets that were placed on the blade surfaces. See the origData_ScanCampaignBladeArrangement.pdf file to see which scan file corresponds to which blade and side. The cloud file names beginning with Friday are for all pressure and suction side scans, and those beginning with Monday are for all leading and trailing edge scans. Note that for the LE and TE scans, the blades were slightly bent under their own weight and in different directions for each edge. No bending was observed in the PS and SS scans. B) The manually preprocessed clouds for each of the three blades. Preprocessing includes unbending of the leading and trailing edge clouds, removal of any non-blade artifacts, aligning/merging of the clouds for the suction and pressure sides to yield only one such cloud per blade, the division of the leading and trailing edge clouds at approximately the leading and trailing edges themselves, and finally, the separate treatment of the tip portion to eliminate the need for automatic alignment at the tip. These clouds are in CloudCompare format (.bin) because the automatic treatment by the developed software was partially conducted using the CloudComPy API. Notes about the WINSENT test site and its data This repository is meant to contain geometric data of the WINSENT turbines: the tower and hub data was closely approximated using a partial laser scan and photos of the standing Northern wind turbine while the blades come from a very careful reconstruction of the Southern turbine's blades. At the time of writing this document, in January 2024, both Northern and Southern turbines are identical by design, and the differences between their blades are expected to lie within the tolerances seen between the three blades of the Southern turbine. For all data coming from the sensors installed on-site, please register and log in here: https://winsent-gui.zsw-bw.de/ For the positions of the two turbines and four met masts, either use the provided 'WINSENT_Test_Site.kml' file (e.g. by loading it in Google Earth) or the following coordinates (precise to the meter): GK3 (Gauß-Krüger, Bessel, Zone 3) Northern Turbine: (3561699, 5392296) Southern Turbine: (3561656, 5392158) NW Metmast: (3561565, 5392292) SW Metmast: (3561520, 5392153) NE Metmast: (3561823, 5392305) SE Metmast: (3561791, 5392165) WGS84 Northern Turbine: Latitude: 9.8365878, Longitude: 48.6652184 Southern Turbine: Latitude: 9.8359836, Longitude: 48.6639818 NW Metmast: Latitude: 9.8347682, Longitude: 48.6651956 SW Metmast: Latitude: 9.8341368, Longitude: 48.6639502 NE Metmast: Latitude: 9.8382723, Longitude: 48.6652871 SE Metmast: Latitude: 9.8378171, Longitude: 48.6640314 The terrain data is also available in the WINSENT_Elevation.tec and WINSENT_Trees_Buildings.tec files, but an official request must be made to receive them. Don't forget to also request the WINSENT_Terrain_Infos.pdf file to have some context on the data. The authors gratefully acknowledge the funding of the project WINSENTvalid (grant no. 03EE2048C) by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). This work has been partially supported by the MERIDIONAL project, which receives funding from the European Union’s Horizon Europe Programme under the grant agreement No. 101084216. The support of Prof. Norbert Haala and Dr. Michael Kölle from the Institute for Photogrammetry and Geoinformatics of the University of Stuttgart for the preparation, execution and postprocessing of the scanning process is also recognized.

The Historic Environment Opportunity Map for New Woodland dataset identifies areas in England that may be suitable for new woodland, based solely on available Historic Environment data. The dataset categorises land by different opportunity ratings to reflect the potential suitability of land for woodland creation while acknowledging areas of uncertainty due to data availability.

The purpose of this dataset is to guide landowners, planners, and decision-makers in considering woodland creation from a historic environment perspective. It should be noted that this dataset only considers the Historic Environment and therefore the opportunity ratings do not guarantee or preclude approval for woodland creation proposals.

As any forestry proposal could have the potential to affect the Historic Environment you should contact your local historic environment service. The local historic environment service can provide further data to support woodland creation proposals.

NHLE is the official, up to date register of all nationally protected historic buildings and sites in England.

SHINE is a single, nationally consistent dataset of non-designated historic and archaeological features from across England that could benefit from land management schemes.

The opportunity ratings are as defined:

· Favourable - Areas deemed suitable for new woodland on consideration of available Historic Environment data.

· Neutral - Areas deemed neither favourable nor unfavourable for new woodland on consideration of available Historic Environment data. Proposals in these areas will require additional consideration of the Historic Environment on a case-by-case basis.

· Unclassified - Areas, where SHINE data has been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective. However, as SHINE data is included in the dataset for this area, a degree of confidence may be inferred when considering the absence of historic environment features.

· Unclassified (No SHINE supplied) - Areas, where SHINE data has not been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective.

· Unsuitable - Areas deemed unsuitable for new woodland on consideration of available Historic Environment data.

Unclassified areas may be suitable or unsuitable for new woodland. To better understand these areas, contact the local historic environment service in accordance with the UKFS and Historic Environment Guidance for Forestry in England - GOV.UK

The datasets included in each opportunity rating are as follows:

Favourable

· Lost Historic Woodlands (ArchAI/Forestry Commission) – An A.I. dataset that identifies areas of woodland depicted on early 20th Century Ordnance Survey mapping which have since been lost.

Neutral

· Historic Parklands (Zulu Ecosystems) – an A.I. dataset that identifies areas of parkland depicted on early 20th Century Ordnance Survey mapping.

· World Heritage Site Core data (Historic England) – Core areas of World Heritage Sites, as designated by UNESCO.

· World Heritage Site Buffer (Historic England) – Buffer zones surrounding World Heritage Sites, as designated by UNESCO.

· Ridge and Furrow (Low) (ArchAI) – an A.I. dataset that identifies areas of less well-preserved historic ridge and furrow derived from LiDAR data.

Unclassified

· HER Boundaries (SHINE supplied) – Geographic areas covered by local historic environment services, where SHINE data has been supplied to the Forestry Commission.

· HER Boundaries (No SHINE supplied) - Geographic areas covered by local historic environment services where SHINE data has not been supplied to the Forestry Commission.

Unsuitable

· Historic Landscape Characterisation (HLC) (local historic environment services) – regional datasets that provide information on the historic character of the landscape.

· Scheduled Monuments (Historic England) – Protected archaeological sites of national importance.

· Scheduled Monuments Buffer – A 20 metre buffer surrounding Scheduled Monuments in-line with UKFS.

· Selected Heritage Inventory for Natural England (SHINE)(local historic environment services) – National dataset of non-designated heritage assets.

· Registered Parks and Gardens (Historic England) – Parks and Gardens designated as being of national significance.

· Registered Battlefields (Historic England) – Battlefields designated as being of national significance.

· Ridge and Furrow (High) (ArchAI) – an A.I. dataset that identifies areas of well-preserved historic ridge and furrow derived from LiDAR data.

Terrestrial laser scanning was used to quantify the vertical growth of two intertidal oyster reefs, built by the non-native Pacific oyster Magallana gigas, in the German Wadden Sea. This dataset includes point cloud surveys of the Kaiserbalje reef (53.6470116° N, 008.2664760° E) and the Nordland reef (53.6424960° N, 008.9411970° E), collected semi-annually from 2020 to 2022. In addition to the raw TLS point cloud data, the dataset includes results from further analyses conducted to assess the spatial and temporal changes in reef structure. The data capture detailed 3D representations of the reefs across multiple seasons, providing valuable insights into the spatial and vertical dynamics of oyster reefs, supporting research on reef growth and morphology in intertidal environments. Methods The dataset was generated using terrestrial laser scanning (TLS) to capture detailed 3D point cloud data of the Kaiserbalje and Nordland reefs, with scans collected semi-annually between 2020 and 2022. Each survey involved approximately 20 individual scans per site, conducted during low-tide periods. The scans were captured using FARO Focus3D models (X130, S120, and Splus150), achieving an accuracy of ±2 mm at a distance of 25 m, with a point spacing of 3–6 mm at 10 m. The spatial analysis concentrated on representative areas approximately 80 × 80 m, covering both central and marginal parts of the reefs. To ensure precise registration of the individual scan point clouds during post-processing, 15 to 17 movable reference spheres (Ø145 mm) were randomly distributed within the survey area. Additionally, 13 to 15 reference spheres were placed on permanently fixed iron rods (900 × 10 mm), embedded in the sediment, to establish a consistent local coordinate system. The positions of the fixed reference spheres were determined using a Stonex-9000-dGPS system with Real Time Kinematic (RTK) corrections and a local geoid model (GCG2016NW), achieving a horizontal accuracy of 8 mm and a vertical accuracy of 15 mm. During post-processing, individual scans were registered into a single ScanScene using the positions of both movable and fixed reference spheres, with each scan clipped to a maximum extent of 30 meters. The registration was performed using the software FARO Scene. The first ScanScene from each series was designated as a reference, with subsequent ScanScenes spatially aligned based on the positions of the fixed reference spheres. Further analysis of the dataset included multiscale model-to-model cloud comparison (M3C2) using 5 cm subsampled clouds from the initial scans as core points. This method, performed with the open-source software CloudCompare, enabled a detailed analysis of the vertical dynamics of the oyster reefs throughout the study period.

One of the National Geospatial-Intelligence Agency’s (NGA) and the National Oceanic and Atmospheric Administration’s (NOAA) missions is to ensure the safety of navigation on the seas by maintaining the most current information and the highest quality services for U.S. and global transport networks. To achieve this mission, we need accurate coastal bathymetry over diverse environmental conditions. The SCuBA program focused on providing critical information to improve existing bathymetry resources and techniques with two specific objectives. The first objective was to validate National Aeronautics and Space Administration’s (NASA) Ice, Cloud and land Elevation SATellite-2 (ICESat-2), an Earth observing, space-based light detection and ranging (LiDAR) capability, as a useful bathymetry tool for nearshore bathymetry information in differing environmental conditions. Upon validating the ICESat-2 bathymetry retrievals relative to sea floor type, water clarity, and water surface dynamics, the next objective is to use ICESat-2 as a calibration tool to improve existing Satellite Derived Bathymetry (SDB) coastal bathymetry products with poor coastal depth information but superior spatial coverage. Current resources that monitor coastal bathymetry can have large vertical depth errors (up to 50 percent) in the nearshore region; however, derived results from ICESat-2 shows promising results for improving the accuracy of the bathymetry information in the nearshore region.

Project Overview
One of NGA’s and NOAA’s primary missions is to provide safety of navigation information. However, coastal depth information is still lacking in some regions—specifically, remote regions. In fact, it has been reported that 80 percent of the entire seafloor has not been mapped. Traditionally, airborne LiDARs and survey boats are used to map the seafloor, but in remote areas, we have to rely on satellite capabilities, which currently lack the vertical accuracy desired to support safety of navigation in shallow water. In 2018, NASA launched a space-based LiDAR system called ICESat-2 that has global coverage and a polar orbit originally designed to monitor the ice elevation in polar regions. Remarkably, because it has a green laser beam, ICESat-2 also happens to collect bathymetry information ICESat-2. With algorithm development provided by University of Texas (UT) Austin, NGA Research and Development (R&D) leveraged the ICESat-2 platform to generate SCuBA, an automated depth retrieval algorithm for accurate, global, refraction-corrected underwater depths from 0 m to 30 m, detailed in Figure 1 of the documentation. The key benefit of this product is the vertical depth accuracy of depth retrievals, which is ideal for a calibration tool. NGA and NOAA National Geodetic Survey (NGS), partnered to make this product available to the public for all US territories. View the SCuBA Info Graphic
All details on how SCuBA was developed, how to access data, and how to use the data, please visit the DOCUMENTATION page.

CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks.

1. It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023.
2. It offers a total of nine sensing modalities, including stereo RGB, depth, monochrome, near-infrared and thermal images, as well as wheel odometry, LiDAR, IMU and GPS-RTK data.
3. It comprises seven sequences collected from three citrus tree fields, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions.
4. It spans a total operation time of 1.7 hours, covers a total distance of 7.5 km, and constitutes 1.3 TB of data.