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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,667 datasets
3,075,010 acres of coastal elevation data were collected by NV5 Geospatial for NOAA from November 2019 to August 2020 using multiple Riegl and Leica sensor systems. The processed data includes classified point clouds in LAS format and 1-meter resolution Digital Elevation Models in GeoTIFF format. This dataset provides topobathymetric coverage, integrating land and seafloor elevations, for the Eastern coasts of Virginia, North Carolina, and South Carolina.
SandGO Dataset is a multimodal resource for embodied AI and robot learning, containing approximately 168 long-horizon trajectory episodes and 38,011 time steps. It was created by XDUImageLab from refined MarsMind_data to support tasks like long-sequence decision-making and instruction following. The dataset was last updated in March 2026.
A dataset titled 'I Wired Mass Surveillance Everywhere' was published on huggingface by author TITAN-2. The description suggests it contains data related to drone operations and surveillance systems. It was last updated on 2026-05-07.
Query Faces for a dataset focused on drone-based person tracking in crowds where individuals have a uniform appearance. The dataset is a 3.7 GB ZIP file published on figshare by Mohamad Alansari under a CC-BY-4.0 license and was last updated on March 17, 2026.
Dropsonde profiles were collected during a NASA airborne campaign in April 2019 to validate satellite and airborne lidar wind measurements. The dataset includes five DC-8 flights totaling 46 hours over the Eastern Pacific and Southwest U.S., using High Definition Sounding System expendable digital dropsondes. It was created by NASA's Langley Research Center Atmospheric Science Data Center to support the calibration and validation of the ESA Aeolus mission and NASA's DAWN and HALO instruments.
Andrew Klekociuk of the Australian Antarctic Division compiled a bibliography detailing 996 references related to Light Detection and Ranging (LIDAR) instruments. The compilation contains entries with fields for year, author, title, and journal. It was recorded as containing 996 references as of June 4, 2007.
A multi-class dataset representing radio frequency interference signatures. It likely contains visual representations of signals from multiple sources, including radar, software-defined radio, and unmanned aerial vehicles. The dataset is hosted on Kaggle, but specific details on its size, creation date, and authorship are not provided.
Michael Barnes conducted research to understand future crew environments for developing unmanned aerial vehicle (UAV) systems. Data from 70 soldiers and experts at Fort Huachuca, Arizona, Fort Hood, Texas, and Hondo, Texas, were collected using human engineering tools like JASS, ECAT, and MicroSaint. The project assessed crew composition, the utility of rated aviators, the addition of imagery specialists, and the use of automation.
An article by Fawaz A. Gerges examining the Egyptian state's response to violent Islamist opposition from groups like al-Jama'a al-Islamiyya and Jihad. The analysis covers the period from 1990 until the Luxor attack in 1997, discussing internal movement divisions, government strategies, and implications for US policy. It details the conflict's costs, including approximately 1,300 casualties and significant damage to the tourism industry.
The mid-twentieth century provides the temporal context for this historical analysis of African American intellectual and political opinion. James H. Meriwether authored this work, which examines attitudes toward seminal events like the anti-apartheid protests, Ghanaian independence, and the Congo crisis. It explores the intersection of domestic civil rights struggles with U.S. foreign relations and transnational solidarity.
Between January 2017 and February 2018, Aurora (via Uber ATG) and the University of Toronto captured this large-scale multi-sensor dataset in the Pittsburgh, PA metropolitan area. It includes data from a 64-beam LiDAR sensor and seven cameras, with ground truth for localization. The dataset spans all four seasons and various weather, time-of-day, and traffic conditions.
The KyFromAbove initiative provides a current basemap for Kentucky, including aerial imagery and LiDAR data. Imagery resolution typically ranges from 6 inches to 3 inches, and LiDAR data meets USGS Quality Level 2 standards. The data is managed by the Kentucky Division of Geographic Information and is available in the public domain.
RESTORE Sponsored Research Project data includes UAS drone survey video and imagery of the ocean surface with corresponding near-surface ocean current measurements. Associated data includes UTM coordinates, ADCP current profiles, CTD temperature and salinity profiles, and GPS boat location data, collected at Galveston Bay and Freeport, Texas. This research is funded by NOAA's RESTORE Science Program under award NA23NOS4510309 to Texas A&M University from 2023 to 2028.
SMAPVEX12 UAVSAR Incidence-Angle Normalized Backscatter Data V001 contains backscatter measurements from the Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument. The data were collected by NASA as part of the Soil Moisture Active Passive Validation Experiment 2012. It supports the calibration and validation of satellite-based soil moisture retrieval algorithms.
An autonomous driving intelligence dataset for computer vision and navigation map applications. The dataset is hosted on Kaggle, but its author, organization, and specific collection details are unknown.
LIDAR-derived imagery was used to map landforms in Seattle, Washington, created primarily by landsliding, including landslide complexes, headscarps, and denuded slopes. The mapping correlates with over 93 percent of approximately 1,300 reported historical landslides, providing spatial density estimates for relative susceptibility. The dataset, summarized by the USGS, offers a tool for landslide hazard reduction in the area.
Topobathymetric lidar point cloud data covering 253,401 acres of coastal Southeast Alaska, collected by NV5 Geospatial for NOAA between June and August 2021. The data includes classifications for ground, water surface, bathymetric bottom, submerged aquatic vegetation, and water column, captured using Leica Hawkeye and Riegl sensor systems. The dataset is delivered in four blocks, each with intensity values, return numbers, time, and scan angle.
492.737 square kilometers of topobathymetric lidar data covering a portion of the Chesapeake Bay in Maryland. The data were collected by NV5 Geospatial, Inc. using a Riegl VQ-880-GH system across nine missions between March 12 and April 19, 2019. The final product includes 38 digital elevation models (DEMs) with 1-meter pixel resolution, derived from classified point clouds with ground, bathymetric bottom, and water column labels.
Topobathymetric lidar data covering 301,150 acres in the Finger Lakes region of New York. The National Oceanic and Atmospheric Administration collected the data using a Riegl VQ-880-G sensor system across 23 missions between September and November 2019. The final dataset includes classified point clouds and 1-meter resolution Digital Elevation Models (DEMs) in GeoTIFF format.
Topobathymetric lidar data covering approximately 564 square kilometers of the Chesapeake Bay near Trappe to Toddville, Maryland. The National Oceanic and Atmospheric Administration (NOAA) collected the data via airborne surveys between November 2018 and April 2019. The final dataset includes classified point clouds and 36 Digital Elevation Models (DEMs) with 1-meter pixel resolution.