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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,676 datasets
168.1 square kilometers of LiDAR bathymetric surface data were collected for shallow seabeds around St. Thomas and St. John, U.S. Virgin Islands. Fugro LADS, NOAA, the University of New Hampshire, and the National Park Service acquired the data during thirteen sorties from January 29 to February 28, 2011. The dataset provides 3x3 meter resolution depth and seafloor reflectivity measurements.
A 5-meter resolution LiDAR intensity mosaic covering 265 square nautical miles of the southwestern Puerto Rico shoreline to approximately 50 meters depth. The Tenix LADS Corporation acquired the data for NOAA from April 7 to May 15, 2006, using a LADS Mk II airborne system. Reflectivity values are scaled logarithmically to an 8-bit integer range.
A benchmark dataset for testing computer vision models in autonomous driving scenarios, specifically focusing on nighttime conditions. The dataset is tagged for object detection, multimodal sensing, and night vision applications. It is described as a train-ready dataset for model testing.
A dataset titled 'Drone and other object' published on Kaggle. The title suggests it contains imagery or data related to drones and other objects, likely for computer vision applications. The author, organization, and specific data characteristics are unknown.
Tii-racing released this fully-annotated collection of high-speed flight data for autonomous and piloted quadrotors in early 2026. The data integrates visual-inertial odometry, motion capture ground truth, and inertial sensor readings for robotics research.
A dataset titled 'uav_detection_merg' is available on Kaggle. The dataset likely contains imagery for detecting Unmanned Aerial Vehicles (UAVs). Metadata is minimal; the specific number of images, annotation details, and creation context are unknown.
nuScenes-v1.0-trainval09_blobs_camera.tgz is a dataset published on Kaggle. The title suggests it contains camera sensor data, likely images, from the nuScenes autonomous driving dataset. The specific content, scale, and features require verification after download.
nuScenes v1.0 is a dataset for autonomous driving research. This specific file likely contains camera image data from the 'trainval' split. The dataset is published on Kaggle, but the original author, organization, and specific details are not provided in the input.
250 million elevation points were collected in May 2004 across a 334 km2 area of South San Francisco Bay. The survey was conducted to support the restoration of 61 km2 of salt ponds to intertidal habitat by the California Coastal Conservancy and partner agencies. This report details the data collection, ground-truthing efforts, and preliminary accuracy assessments.
nuScenes-v1.0-trainval06_blobs_camera.tgz is a dataset file published on Kaggle. The title suggests it contains camera sensor data, likely images, from the nuScenes autonomous driving dataset. The specific content, scale, and structure require verification after download.
nuScenes-v1.0-trainval05_blobs_camera.tgz is a dataset file from the nuScenes collection, a popular benchmark for autonomous driving research. The title suggests it contains camera sensor data, likely images, from the nuScenes v1.0 release. It is hosted on Kaggle, but detailed metadata such as the number of images, specific camera types, and collection details are not provided in the input.
nuScenes-v1.0-trainval08_blobs_camera.tgz is a dataset file from the nuScenes autonomous driving collection, published on Kaggle. The title suggests it contains camera sensor data, likely images, from the nuScenes v1.0 benchmark's training and validation splits. The specific content, scale, and other metadata require verification after download.
nuScenes-v1.0-trainval07_blobs_camera.tgz is a dataset from the nuScenes collection, published on Kaggle. The title suggests it contains camera sensor data, likely images, intended for the training and validation of autonomous vehicle perception systems. The specific content, scale, and structure require verification after download.
nuScenes is a public large-scale dataset for autonomous driving. This specific file, v1.0-trainval02_blobs_camera.tgz, likely contains camera image data from the nuScenes collection. The dataset is hosted on Kaggle, but detailed metadata about its exact content and scale is unavailable.
nuScenes is a large-scale dataset for autonomous driving research. This specific file, v1.0-trainval04_blobs_camera.tgz, likely contains camera image data from the training and validation splits. The dataset was published on Kaggle, but detailed metadata such as author, license, and update date is not provided.
nuScenes is a large-scale dataset for autonomous driving. This specific file likely contains camera image data from the nuScenes v1.0 trainval split. The dataset is published on Kaggle, but detailed metadata is unavailable.
nuScenes is a large-scale dataset for autonomous driving. This specific component likely contains camera image data and associated metadata from the nuScenes v1.0 trainval01 split. The dataset is hosted on Kaggle, but detailed specifications are not provided in the minimal metadata.
A 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho was surveyed monthly between 2021 and 2025. The dataset provides raw lidar data collected as part of a multi-year effort to monitor snow distribution. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.
A multi-year effort from 2021 to 2025 monitored monthly snow distribution over a 35 km² region in Idaho's Boise Mountains. The dataset provides digital terrain models (DTM), digital surface models (DSM), snow depth models, and canopy height models (CHM) derived from airborne lidar point clouds. Data acquisition in 2021 overlapped with the NASA SnowEx 2021 field campaign.
Carla Test Dataset comprises 200 images likely sourced from the CARLA autonomous driving simulator. The dataset is published on Kaggle, but details about its creator, collection date, and specific image content are not provided. Its title suggests it is intended for testing computer vision models within a simulated driving environment.