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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,023 datasets
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.
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.
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.
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.
LIDARDS1 is a dataset hosted on Kaggle. Its title suggests it contains lidar sensor data, which is commonly used in robotics and autonomous vehicle perception. The dataset's specific content, size, and origin are not detailed in the provided metadata.
VisDrone-Det-COCO is a dataset for object detection tasks, likely derived from the VisDrone benchmark. It appears to be formatted in the COCO annotation standard. The dataset is hosted on Kaggle, but its specific size, creation date, and author are not provided in the available metadata.
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.
UAVDT Resume Checkpoint is a dataset for computer vision tasks, likely involving imagery from Unmanned Aerial Vehicles (UAVs). The dataset is hosted on Kaggle, but its specific contents, scale, and creation details are not provided in the available metadata. Its title suggests a focus on object detection or tracking, potentially serving as a checkpoint or subset of a larger UAV benchmark.
LIDAR vertical profiles captured stratospheric temperature and density over Antarctica to study Polar Stratospheric Clouds. The IFA/CNR instrument at McMurdo Base collected data from 1991 to 1992, with 30-minute measurements approximately every four days. Profiles cover altitudes from 25 km up to 40 km.
Text chunks from multiple religions intended for use in AI debate systems. The dataset is hosted on Kaggle, but the original author, organization, and creation date are unknown. The raw description indicates the data is chunked, but the specific content, size, and structure are not detailed.
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.
5,065 LAS tiles of combined topographic and bathymetric lidar data covering 285,529 acres of the Florida Keys. The data were collected by Quantum Spatial, Inc. for NOAA across 23 missions between November 2018 and March 2019, following Hurricane Irma. It includes detailed classifications for ground, water surface, bathymetric bottom, water column, and submerged features.
964.624 square kilometers of coastal and submerged topography for the Chesapeake Bay area, collected by NV5 Geospatial, Inc. for NOAA in 15 missions between February and April 2019. The final product includes 53 digital elevation models (DEMs) with 1-meter pixel resolution, derived from classified lidar point clouds. Data points are classified according to ASPRS standards, including ground, water column, and bathymetric bottom categories.