Real-world maritime LiDAR dataset for ship detection. The dataset was collected across a busymaritime environment, including the marina and the River Thames. A Velodyne VLP-16 LiDAR with 16 channels and a measurement range of up to 100m was employed. The processed dataset is managed in the same way asthe KITTI datasets

No description found

This dataset contains derived data from the profiling lidar (Windcube v2.1) deployed at WFIP3's SHIP. The derived data files herein are based on the lidar's STA files (i.e., the 10-min average data files). The data have been corrected for the motion of the ship.

OWLETS1_SurfaceLidar_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-1) lidar data collected at the NASA Langley Research Center ground site and Chesapeake Bay Bridge Tunnel site during the OWLETS field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete. Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.

This dataset contains processed data from NREL's Halo XR+ #235 scanning lidar at WFIP3's NOAA ship site. The location of the ship is provided separately. The data have NOT been corrected for the motion of the ship.

This dataset is a LAS dataset containing light detection and ranging (lidar) data and multibeam sonar data representing the beach and near-shore topography of Lake Superior at the Duluth entry, Duluth, Minnesota. The LAS dataset was used to create a digital elevation model (DEM) of the approximately 1.87 square kilometer surveyed area. Lidar data were collected September 23, 2020 using a boat mounted Velodyne unit. Multibeam sonar data were collected September 22-23, 2020 using a Norbit integrated wide band multibeam system compact (iWBMSc) sonar unit. Methodology similar to Wagner, D.M., Lund, J.W., and Sanks, K.M., 2020 was used.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [8], averaging time [10-15 sec], chosen SNR threshold [-20 dB].

This repository contains voyage data for the manuscript “Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON, and the ERA5 and MERRA-2 reanalyses” (DOI: 10.5281/zenodo.14071808), which have not already been published elsewhere. It includes ceilometer, radiosonde and surface observations collected by the University of Cantebrury, Christchurch, Aotearoa/New Zealand and collaborators on the following Southern Ocean voyages [abbreviation, ship name (time period, track)]:

• AA15-16: RSV Aurora Australis (22 October 2015–22 February 2016, Hobart, Tasmania, AU–East Antarctica–Macquarie Island–Hobart, Tasmania, AU)
• HMNZSW16: HMNZS Wellington (20 November 2016–20 December 2016, Auckland, NZ–Ross Sea–Auckland, NZ)
• NBP1704: RV Nathaniel B. Palmer (11 April 2017–14 June 2017, Lyttelton, NZ–Terra Nova Bay–Lyttelton, NZ)
• TAN1502: RV Tangaroa (20 January 2015–12 March 2015, Wellington, NZ–Ross Island–Wellington, NZ)
• TAN1702: RV Tangaroa (16 March 2017–1 April 2017, Wellington, NZ–Campbell Plateau–Wellington, NZ)

See also Table 1 in the manuscript for more information.

Included are data from the following instruments (abbreviation, instrument name, file format):

• chm15k: Lufft CHM 15k ceilometer (NetCDF)
• cl51: Vaisala CL51 ceilometer (DAT)
• rs: Radiosonde profiles (MATLAB and InterMet Systems)
• surf: Surface observations from the ship information system and weather instruments (CSV, MATLAB and NetCDF)

The ZIP archives in this repository are named “voyage abbreviation_instrument.zip”.

The ceilometer files can be processed with the ALCF (https://alcf.peterkuma.net, DOI: 10.5281/zenodo.3774785, and https://github.com/alcf-lidar/alcf). The CL51 files can be converted to NetCDF with cl2nc (https://github.com/peterkuma/cl2nc, DOI: 10.5281/zenodo.4409715). The InterMet Systems radiosonde files can be converted to NetCDF with rstool (https://github.com/peterkuma/rstool, DOI: 10.5281/zenodo.5040217).

If you use the data in this repository, the following attribution is required:

• AA15-16: Australian Antarctic Division and University of Canterbury.
• HMNZSW16: Royal New Zealand Navy and University of Canterbury.
• NBP1704: National Science Foundation, Cooperative Institute for Research in Environmental Sciences, University of Colorado and University of Canterbury.
• TAN1502 and TAN1702: National Institute of Water and Atmospheric Research and University of Canterbury.

See also the Acknowledgements section in the manuscript for details.

OWLETS2_SurfaceLidar_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) NASA GSFC TROPOZ, NASA LMOL, and wind lidar data collected at Hart Miller Island site and UMBC site during the OWLETS-2 field campaign. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data collection is complete. Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.

A topographic lidar survey was conducted from September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5-10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. The data were collected at a nominal pulse space of 1-meter (m) and processed to identify bare earth elevations. Bare earth Digital Elevation Models(DEMs) were generated based on these data. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process the lidar data. The bare earth DEMs are 32-bit floating point ERDAS Imagine (IMG) files with a horizontal spatial resolution of 1-m by 1-m. They are projected to UTM zone 15N or 16N NAD83 meters. Their vertical datum is NAVD88 (GEOID12) meters. The DEMs are organized on a 2-kilometer (km) by 2-km tiling scheme that covers the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

NOAA ETL deployed the Depolarization and Backscatter - Unattended Lidar (DABUL) LIDAR aboard the SHEBA ship. This data set contains final 10 minute cloud property and aerosol ASCII data files for the entire deployment period.

The global maritime collision avoidance and object detection market is experiencing robust growth, driven by increasing maritime traffic, stringent safety regulations, and advancements in sensor technologies like LiDAR and radar. The market, currently valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by the rising adoption of autonomous navigation systems and the increasing demand for enhanced situational awareness to prevent accidents and minimize operational risks. The integration of AI and machine learning algorithms further enhances the capabilities of these systems, enabling more precise object detection and prediction of potential collisions, even in challenging environmental conditions. The military segment dominates the market due to the critical need for advanced collision avoidance systems in naval operations, while the civil segment is witnessing rapid growth fueled by increasing commercial shipping activities and the adoption of autonomous vessels. Key players such as Orlaco, Raytheon, Garmin, and Velodyne are actively investing in research and development to improve the performance and functionalities of their products. Technological advancements focusing on improved sensor accuracy, reduced latency, and enhanced data processing capabilities are expected to further stimulate market growth. The market segmentation reveals a strong preference for LiDAR and radar technologies, which offer superior accuracy and reliability compared to other sensing methods. North America and Europe currently hold the largest market shares, driven by robust technological advancements and stringent safety regulations within their respective maritime industries. However, Asia-Pacific is expected to witness significant growth in the coming years, owing to the rapid expansion of its shipping and maritime sectors, coupled with increasing government investments in maritime infrastructure and safety improvements. The key restraints to market growth include the high initial investment costs associated with installing and maintaining collision avoidance systems and the integration challenges associated with legacy systems. Nevertheless, the considerable benefits in terms of safety and cost savings from accident prevention are expected to outweigh these constraints, ultimately driving sustained market growth throughout the forecast period.

This data set is the airborne scanning LiDAR of a suite of different instruments deployed during the Sea Ice Physics and Ecosystems eXperiment (SIPEX) in 2007. Surveys have been flown over sea ice between 110-130 degrees E as part of the Australian Antarctic science project 2901.

Public Summary for project 2901 This research will contribute to a large multi-disciplinary study of the physics and biology of the Antarctic sea ice zone in early Spring 2007. The physical characteristics of the sea ice will be directly measured using satellite-tracked drifting buoys, ice core analysis and drilled measurements, with detailed measurements of snow cover thickness and properties. Aircraft-based instrumentation will be used to expand our survey area beyond the ship's track and for remote sampling. The data collected will provide valuable ground-truthing for existing and future satellite missions and improve our understanding of the role of sea ice in the climate system.

Project objectives: (i) to quantify the spatial variability in sea ice and snow cover properties over scales of metres to hundreds of kilometres in the region of 110-130 degrees E, in order to improve the accuracy of sea ice thickness estimates from satellite altimetry and polarimetric synthetic aperture radar (SAR) data. (ii) To determine the drift characteristics, and internal stress, of sea ice in the region 110-130 degrees E. (iii) To investigate the relationships between the physical sea ice environment and the structure of Southern Ocean ecosystems (joint with AAS Proposal 2767).

This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; https://doi.org/10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [8], averaging time [4 sec], chosen SNR threshold [-19 dB].

This data set contains SABL (Scanning Aerosol Backscatter Lidar) field DORADE format files from the ship R/V Kaiyo during the Tropical Ocean Climate/Japan Agency for Marine-Earth Science and Technology, South Pacific (TOCS/JAMTEC) experiment.

This data shows the depth of the seabed around Ireland. The data was collected in 1996 and between 2000 and 2022. Bathymetry is the measurement of how deep is the sea. Bathymetry is the study of the shape and features of the seabed. The name comes from Greek words meaning "deep" and “measure". Bathymetry is collected on board boats working at sea and airplanes over land and coastline. The boats use special equipment called a multibeam echosounder. A multibeam echosounder is a type of sonar that is used to map the seabed. Sound waves are emitted in a fan shape beneath the boat. The amount of time it takes for the sound waves to bounce off the bottom of the sea and return to a receiver is used to determine water depth. LiDAR (Light Detection and Ranging) is another way to map the seabed, using airplanes. Two laser light beams are emitted from a sensor on-board an airplane. The red beam reaches the water surface and bounces back; while the green beam penetrates the water hits the seabed and bounces back. The difference in time between the two beams returning allows the water depth to be calculated. LiDAR is only suitable for shallow waters (up to 30m depth). The data are collected as points in XYZ format. X and Y coordinates and Z (depth). The boat travels up and down the water in a series of lines (trackline). An XYZ file is created for each line and contains thousands of points. The line files are merged together and converted into gridded data to create a Digital Terrain Model of the seabed. Colours are also used to show depth ranges. These are raster datasets. Raster data stores information in a cell-based manner and consists of a matrix of cells (or pixels) organised into rows and columns. The format of the raster is a grid. The grid cell size varies. The highest resolution is 10m by 10m. This means that each cell (pixel) represents an area on the seabed of 10 metres squared. Each cell has a depth value which is the average depth of all the points located within that cell. This data shows areas that have been surveyed. There are plans to fill in the missing areas between 2020 and 2026. The deeper offshore waters were mapped as part of the Irish National Seabed Survey (INSS) between 1999 and 2005. INtegrated Mapping FOr the Sustainable Development of Ireland's MArine Resource (INFOMAR) is mapping the inshore areas. (2006 - 2026).

This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [8], averaging time [1.5 sec], chosen SNR threshold [-17 dB]. The measurements were performed during the Polarstern cruise PS85 funded by the Alfred Wegener Institute under Polarstern grants AWI_PS85_01. The research was funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung–BMBF) as part of the project "Laptev-Sea Transdrift" under grant 03G0833D.

This dataset contains SABL (Scanning Aerosol Backscatter Lidar) field dorade format files obtained by the ship ORV SAGAR KANYA during the Indian Ocean Experiment First Field Phase (INDOEX-98).

Gridded multibeam bathymetry is integrated with gridded LiDAR bathymetry and bathymetry derived from multispectral IKONOS satellite data. Gridded (5 m cell size) multibeam bathymetry collected aboard NOAA Ship Hiialaka'i and R/V AHI. Lidar bathymetry were collected by the Naval Oceanographic Office. Bathymetry values shallower than 25 m were derived by gauging the reletive attenuation of blue and green spectral radiance as a function of depth. A multiple linear regression analysis of linearized blue and green band spectral values against depth determined the variables of y-intercept, blue slope and green slope values. Variables then used in multivariate slope intercept formula to derive depth. Variables were adjusted to improve the statistical accuracy and spatial coverage of the final derived bathymetry product. Digital image processing to derive depths conducted with the ENVI 4.5 software program while data editing and integration was performed using ArcGIS 9.3. This dataset is for the shelf environment of Saipan Island, Commonwealth of Northern Mariana Islands, USA