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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,023 datasets
A 2021 pilot survey mapped the nearshore areas of Dundrum Bay and Carlingford Lough in Northern Ireland. It includes bathymetric LiDAR data and natural colour orthophotography captured simultaneously. The dataset is published by OpenDataNI under the OGL-UK-3.0 license.
A 2021 pilot bathymetric LiDAR survey mapped the nearshore areas of Dundrum Bay and Carlingford Lough in Northern Ireland. Natural Colour Orthophotography was captured simultaneously with the LiDAR data. The dataset is provided by OpenDataNI under the OGL-UK-3.0 license and was last updated in March 2026.
Indian Road Driving Dataset is the largest open dataset of annotated Indian road footage, created by ThirdEye Labs. It addresses a critical gap by capturing unique challenges like dense mixed traffic, auto-rickshaws, and cattle, which are absent from datasets like BDD100K and nuScenes.
LiDAR and RGB orthophotos from 2009 cover the entire Snohomish River estuary. The dataset supports comprehensive abiotic and biotic monitoring of the Qwuloolt estuarine levee breach restoration at a 150-hectare site. It was published by the National Oceanic and Atmospheric Administration, Department of Commerce.
Replication data for a forthcoming article in the journal Comparative Politics. The dataset was authored by Phillip Ayoub and hosted on Harvard Dataverse. It was last updated on April 28, 2026.
A 62.3 KB dataset containing comparative analysis of UAV-enabled IoT and 6G communication frameworks. The data, authored by M. Rudra Kumar, includes tags related to high data rates, Terahertz communications, and UAV positioning and optimization.
41.5 KB dataset from M. Rudra Kumar, last updated in March 2026. It contains data related to optimizing Unmanned Aerial Vehicle (UAV) and Reconfigurable Intelligent Surface (IRS) height for maximizing signal strength in Terahertz communications. The dataset supports research into improving propagation conditions and achieving high data rates in next-generation wireless networks.
System architecture data for a Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication network. The dataset, shared by author M. Rudra Kumar in March 2026, focuses on enhancing Terahertz communications and UAV positioning to improve propagation conditions and achieve high data rates.
A dataset of spectrograms likely used for detecting drones via acoustic or radio frequency signals. It was published on Kaggle, but the specific collection date, creator, and data volume are unknown. The columns and sample data are unavailable, limiting immediate assessment of its structure.
WUAV-data is a dataset hosted on Kaggle. The dataset's content is inferred to be related to drones or unmanned aerial vehicles, based on the title. No further metadata, such as columns, size, or license, is available.
Drone images aggregated from multiple sources, published on Kaggle. The dataset is associated with platform tags for safety, robotics, and accident analysis, suggesting a focus on aerial observation. Specific details on the number of images, collection dates, and original authors are not provided in the available metadata.
Multi-Perspective Dataset of Plains Zebras provides synchronized per-frame telemetry from four DJI Mini 4 Pro drones operating simultaneously. The data was collected by author edouard-rolland at Ol Pejeta and was last updated on March 23, 2026.
Paul Lushenko's dataset, hosted by Harvard Dataverse, investigates how target identities shape public perceptions of drone strike legitimacy. The dataset was last updated on April 27, 2026. Its specific content likely contains survey or experimental data related to public attitudes towards military technology.
NVIDIA released 918 dynamic neural reconstructions of driving scenes in March 2026, each lasting approximately 20 seconds. The data consists of .usdz files and surface meshes generated from a specialized six-camera sensor suite.
Data collected for NOAA's Deepwater Horizon Lessons Learned Studies on detecting oil thickness and emulsion mixtures. The research involved synoptic collection of satellite and airborne imagery, surface oil characterization, and subsurface data at the MC20 site, which has a chronic oil discharge. This field research was primarily funded by the U.S. Department of the Interior and NOAA through an interagency agreement.
2016-2018 data collected as part of NOAA's Deepwater Horizon lessons learned study on oil slick detection. The dataset contains remote sensing imagery and field data from the Mississippi Canyon lease block #20 (MC20), a site of a chronic oil discharge. Research was funded by the U.S. Department of the Interior, Bureau of Safety and Environmental Enforcement, and NOAA.
LiDAR data collected by the Scottish public sector is available as point clouds and derived terrain models. The dataset comprises multiple subsets commissioned for different organizational requirements, with details accessible via a provided remote sensing portal. Data is offered under the Open Government Licence v3, with a non-commercial exception for one specific phase.
A dataset from the Hugging Face platform by author moonjongsul, last updated on 2026-05-07. The title suggests it relates to kitting operations in manufacturing, likely involving objects that are flipped. The specific content, scale, and collection method are not detailed in the provided metadata.
Airborne imaging data from the G-LiHT mission provides information on aircraft attitude, altitude, and view angles. The data is processed as GeoTIFF rasters at 1-meter spatial resolution for local areas across North America. It is produced by NASA's Goddard Space Flight Center.
SAR UAV Results is a dataset published on HuggingFace by author duy95. The title suggests it contains results from Synthetic Aperture Radar (SAR) sensors mounted on Unmanned Aerial Vehicles (UAVs). The dataset was last updated on 2026-05-07.