Loading...
Loading...
Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,667 datasets
Featuring results from nonparametric tests comparing UAV unit time to road traffic unit time at various time points. The data is provided in an XLS file of 5.5 KB, authored by Xingbo Long and last updated in March 2026. The specific row count and column structure are not detailed in the input.
This dataset compares road traffic unit time measurements from Tencent Map and drone surveillance at nine different time points over 30 consecutive days. It includes results of normality tests for the data. The dataset is provided in an XLS file with a size of 5.5 KB.
LET Dataset is a collection of real-world multi-task data from the Kuavo 4 Pro full-size humanoid robot, covering multiple scenarios and operation types. It was created by LejuRobotics and last updated on March 13, -2026. The dataset is designed to support scalable robot learning in real environments.
A 5.5 KB Excel file details a hierarchical Model Predictive Control architecture for robotic arms. The data likely contains simulation results for path planning and obstacle avoidance in unpredictable environments. It was authored by Jiexin Wang and last updated on March 19, 2026.
Multiview video data of human activity, both scripted and unscripted, collected with roughly 100 actors over several weeks. The current release consists of about 328 hours (516GB, 4259 clips) of video data from 29 cameras, plus 4.6 hours of UAV data and annotations for roughly 184 hours. The dataset was created by Kitware, with further updates planned.
2.3 million text chunks sourced from the writings of four major world religions. The dataset likely contains segmented passages from Christian, Jewish, Hindu, and Islamic texts, facilitating comparative analysis. Its origin, collection method, and specific textual sources are not detailed in the provided metadata.
Time-series atmospheric profiles from the Phoenix Mars Lander, with durations between 5 and 90 minutes. The data contains raw laser scattering profiles at 532nm and 1064nm wavelengths, with each profile representing an accumulation over 1.28 to 20.24 seconds. It was produced by the National Aeronautics and Space Administration and last updated in March 2026.
NOAA and USGS provide a 0.5-meter bare-earth raster digital elevation model (DEM) derived from Quality Level 1 lidar data collected between 2020 and 2023. The dataset consists of 16,760 individual 500-meter x 500-meter GeoTIFF tiles covering approximately 1,428 square miles across the islands of Kahoolawe, Lanai, Maui, Molokai, and Oahu. Data acquisition followed the National Geospatial Program Lidar Base Specification Version 2.1 with an aggregate nominal pulse spacing of 0.35 meters.
Morro Bay, California, is covered by a 1-meter resolution digital elevation model derived from topobathymetric lidar data. The dataset covers approximately 4,215 acres and consists of 96 tiles, collected by NV5 Geospatial for NOAA's Office of Coastal Management in June 2022. The data includes classified point clouds with categories for ground, water surface, bathymetric bottom, water column, and submerged objects like oyster reefs.
Morro Bay, California, 2022 topobathymetric lidar data covering approximately 4,215 acres along the south central Pacific Coast. The dataset, collected by NV5 Geospatial for NOAA OCM and the NEP, includes classified LAS point clouds and 1-meter resolution bare earth Digital Elevation Models (DEMs). Data were acquired on June 14, 2022, and are delivered in 500 m x 500 m tiles clipped to the project boundary.
177 acres of high-density lidar data were collected over the Grand Bay National Estuarine Research Reserve using a drone-mounted Velodyne Puck VLP-16 system. Quantum Spatial and PrecisionHawk conducted flights from May 9-11, 2017, achieving an average first return density over 105 points per square meter. The deliverables include lidar point clouds in LAS/LAZ format with UTM zone 16 NAD83(2011) horizontal and NAVD88 vertical datums.
Chesapeake Bay, Maryland, is covered by two lidar datasets (MD1902 and MD1903) collected by NV5 Geospatial in November 2019. The data includes classified point clouds and derived 1-meter resolution digital elevation models (DEMs) covering approximately 260 and 273 square kilometers respectively. The National Oceanic and Atmospheric Administration (NOAA) processed the lidar data into GeoTIFF format.
2023 NOAA NGS Lidar data for Long Island Sound, NY, collected in four blocks across 69 missions flown between March and October 2023. The dataset includes topobathymetric point clouds in LAS 1.4 format with 18 distinct classification codes and 4 channel bits for sensor identification. Data features lidar intensity values, number of returns, return number, time, and scan angle, compiled into 500 m x 500 m tiles.
3,075,010 acres of coastal terrain and seafloor were surveyed using multiple airborne lidar systems from November 2019 to August 2020. The National Oceanic and Atmospheric Administration (NOAA) collected this point cloud data, which includes intensity values, return numbers, scan angles, and time stamps. Data is formatted in LAS 1.4 with ASPRS-standard classifications.
311 square miles of topobathymetric lidar data covering Tampa Bay, Florida, collected by Leading Edge Geomatics for NOAA. The data were acquired from November 22 to December 20, 2019, and are delivered in two blocks totaling 1,788 tiles. The point cloud data is in LAS 1.4 format with classifications for ground, water surface, bathymetric bottom, and other features.
4,821 LAS tiles covering approximately 964.624 square kilometers of the Chesapeake Bay near Gwynn to Newport News, Virginia. The data were collected by NV5 Geospatial, Inc. using a Riegl VQ-880-GH system in 15 missions between February 17 and April 12, 2019. It includes point classifications for ground, bathymetric bottom, water column, and other features, along with intensity, return number, time, and scan angle.
253,401 acres of combined topographic and bathymetric lidar data were collected for Southeast Alaska's Revillagigedo Channel between June and August 2021. The National Oceanic and Atmospheric Administration (NOAA) commissioned NV5 Geospatial to acquire the data using Leica Hawkeye 4X and Riegl 1560i systems. The final dataset is delivered in four blocks, each containing LAS format point clouds with ASPRS-standard classifications for ground, water surface, bathymetric bottom, submerged vegetation, and water column.
301,150 acres of topobathymetric lidar data covering the Finger Lakes region of New York, collected by the National Oceanic and Atmospheric Administration (NOAA) between September and November 2019. The dataset includes classified point clouds in LAS format with categories for ground, water surface, bathymetric bottom, and submerged aquatic vegetation, as well as derived 1-meter resolution digital elevation models (DEMs). Data were collected over 23 missions using a Riegl VQ-880-G sensor system and are tiled in 500m x 500m units.
2013 NOAA NGS LIDAR of New Jersey: Barnegat Light is a topobathy point cloud dataset collected by the National Oceanic and Atmospheric Administration's National Geodetic Survey. The data was acquired over two days in September 2013 and includes classified points for ground, water, bathymetry, and noise, along with lidar intensity and encoded RGB values. It is stored in LAS 1.2 format and covers the Barnegat Light area in Ocean County, New Jersey.
2018-2019 NOAA NGS topobathymetric lidar data collected by Quantum Spatial, Inc. across 85 missions from November 2018 to March 2019. The project covers approximately 1,381,270 acres from Miami to the Marquesas Keys, Florida, and is delivered as 23,926 LAS tiles. Data includes point classifications for ground, water surface, bathymetric bottom, water column, and other features.