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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,023 datasets
VisDrone2019-MOT-val-CV4 is a validation subset from the VisDrone2019 dataset, published on Kaggle. The dataset likely contains video sequences captured by drones for multi-object tracking tasks. Its specific content, scale, and creation details require verification after download.
334 square kilometers of Colorado watersheds were surveyed by the NEON Airborne Observation Platform using an Optech Gemini LiDAR instrument. The data includes classified point clouds in LAZ format and 1-meter resolution raster files for surface elevation, terrain elevation, and canopy height. These data were contracted by Lawrence Berkeley National Laboratory and are also accessible via Google Earth Engine.
Grand Slams 2023-2025 is a dataset published on Kaggle. It likely contains match results and statistics from the four major tennis tournaments—the Australian Open, French Open, Wimbledon, and US Open—over the specified three-year period. The dataset's author, organization, and specific contents are not detailed in the available metadata.
A dataset hosted on Kaggle with the title 'kitti-synthetic-vo'. The title suggests it contains synthetic visual odometry sequences, likely related to the KITTI benchmark for autonomous driving. The dataset's author, organization, size, and specific contents are unknown from the provided metadata.
A dataset published on Kaggle with the title 'ZiplineDroneDataAnalysis_SummaryData'. The dataset likely contains summary statistics or operational metrics related to drone delivery or flight operations. Its specific content, origin, and temporal coverage require verification after download.
Grand Slam 2023-2025 is a dataset published on Kaggle. Its title suggests it contains information related to the four major tennis tournaments—the Australian Open, French Open, Wimbledon, and US Open—across the 2023 to 2025 seasons. The dataset's specific content, such as match results, player statistics, or point-by-point data, requires verification after download.
nuScenes v1.0 keyframes provide synchronized Camera, LiDAR, and Radar data for autonomous vehicle perception. The dataset likely contains annotated scenes captured from a vehicle platform. It is a standard benchmark for multi-sensor object detection and tracking tasks in robotics.
Shadi Hamid, a fellow at the Brookings Institution, argues that mainstream Islamist parties in Arab countries may deliberately avoid winning elections. The dataset likely contains analysis of electoral strategies and political behavior from a scholarly perspective. It is sourced from the paperswithcode platform, which aggregates academic and research datasets.
VisDrone is a dataset hosted on Kaggle. Its title suggests it contains visual data captured by drones, likely for computer vision tasks. The dataset's specific contents, scale, and origin require verification after download due to minimal provided metadata.
Drone imagery likely intended for computer vision tasks. The dataset has been cleaned, though the specific scope and cleaning methodology are not detailed. It is hosted on Kaggle, but the author, organization, and last update date are unknown.
A processed dataset derived from the CARLA autonomous driving simulator. The data likely contains features relevant to training and evaluating machine learning models for self-driving vehicles. It was published on Kaggle, but the original author, organization, and specific data collection details are unknown.
A dataset titled 'Drones-Deeponet-Dawama-Dataset' is hosted on Kaggle. The dataset's content likely relates to drone operations or robotics, given the title. No further metadata regarding its creation, size, or structure is provided.
A curated dataset of real-world drone imagery formatted for YOLO object detection architectures. The dataset is sourced from Kaggle and focuses on aerial imagery. Specific details on size, creation date, and authorship are not provided.
Dawama-UAV-Dataset is a collection of aerial imagery likely captured by unmanned aerial vehicles. The dataset is hosted on Kaggle, but its specific contents, size, and creation details are not provided in the available metadata. Further details such as the number of images, annotation types, and the creator are unknown.
Lidar1 is a dataset hosted on Kaggle, indicated by platform tags to contain geospatial and point cloud data. The dataset's specific content, size, and origin are not detailed in the available metadata.
car_accident_carla2 is a dataset hosted on Kaggle. Its title suggests it contains data related to vehicle accidents, likely generated using the CARLA autonomous vehicle simulation platform. The dataset's specific contents, scale, and creation details are not provided in the available metadata.
Henning Finseraas produced this replication dataset for a study in The Journal of Politics, featuring results from a conjoint experiment conducted in Britain and Norway. The data measures voter responses to policy, social, and group-based appeals, specifically focusing on social class and out-group conflict.
VisDrone2019-DET_filtered_tiny-object_Images is a subset of the VisDrone2019 dataset, likely focusing on small-scale objects in aerial imagery. The dataset is hosted on Kaggle, but its specific content, size, and creation details are not provided in the metadata. Columns and sample data are unknown, requiring direct inspection of the dataset files for verification.
An index of LiDAR files for the City of Winnipeg, created by the Water and Waste department. Each file contains a point cloud detailing location, elevation, and surface type, with data organized by year and file type. The index includes links to individual 1km x 1km tiles and full annual collections.
Diffusion Path Planning Data is a dataset hosted on HuggingFace by author Zharon. The dataset's content and structure are inferred from its title to relate to robotic path planning using diffusion models. It was last updated on April 14, 2026.