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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,672 datasets
A dataset titled 'WildFireUAV' is hosted on Kaggle. The dataset likely contains imagery or sensor data collected by Unmanned Aerial Vehicles (UAVs) for wildfire-related applications. Metadata such as column descriptions, sample data, and size are unavailable, requiring verification after download.
Real-time flow field data related to Unmanned Aerial Vehicle (UAV) aerodynamics. The dataset is hosted on Kaggle, but specifics on size, collection method, and authorship are not provided in the metadata. Users must download the dataset to inspect its full structure and content.
Woodland structural data derived from LiDAR for the year 2011 in the Isle of Wight contains metrics like height, foliage height diversity, mean crown area, and tree count for woodlands under 1 hectare. It was collected by the Environmental Information Data Centre under a NERC grant to study how adjacent older woodland affects structure in recent plantings. The dataset includes variables for bedrock, elevation, age, aspect, and slope.
Fernando Mendes Pereira from Universidade Estadual Paulista reviews multiple methods for propagating guava (Psidium guajava L.), including seed, layering, grafting, cuttings, and tissue culture. The review addresses commercially adopted methods, progress in recent years, and differing adoption levels between producing countries. Needs for improvement in guava tree production are also discussed.
CARLA is an open-source simulator for autonomous driving research. This dataset likely contains data generated within the CARLA simulation environment, such as sensor readings, vehicle trajectories, or scenario logs. It is hosted on Kaggle, a platform for sharing datasets.
Force plate data from a study on tripedal locomotion in canine amputees. The dataset, created by Zoe T. Self Davies, was last updated in April 2026. It is provided as supplementary material for research on biomechanical adaptations.
Phase data from a study on locomotion in three-legged dogs, analyzing tripedal gaits. The dataset was created by Zoe T. Self Davies and was last updated in April 2026. It is shared under a CC-BY-4.0 license on the figshare platform.
A dataset from Kaggle concerning maize crops in 2025. It likely contains UAV-captured imagery and related data for analyzing water stress and common rust disease. The specific data volume, collection methodology, and geographic origin require verification after download.
North East England coastal lidar data from 2023, collected by the National Network of Regional Coastal Monitoring Programmes of England. The dataset contains 1,377 records. It is aggregated via the Government Digital Service's eu_open_data platform.
KITTI_dataset is a dataset hosted on Kaggle. The title suggests it contains data related to autonomous driving and computer vision, likely from the KITTI benchmark suite. The dataset's specific contents, such as the number of samples or included features, are unknown from the provided metadata.
Ferdib-Al-Islam and colleagues from Northern University published this dataset in July 2021. It contains real images of people, with 150 images for the 'with mask' class and 150 images for the 'without mask' class. The dataset is hosted on Zenodo and is intended for computer vision research.
CALIPSO satellite data provides 5 km aerosol layer profiles collected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The mission, launched on April 28, 2006, is a joint project between NASA and CNES to study clouds and aerosols. It flies within the international A-Train constellation for coincident Earth observations.
CALIPSO satellite data from the Wide Field Camera instrument, providing 1 km registered science observations. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission was launched on April 28, 2006 by NASA and CNES to study clouds and aerosols. It operates within the international A-Train constellation for coincident Earth observations.
An open multi-sensor dataset for autonomous driving research created by Audi AG. It contains semantically segmented images, semantic point clouds, and 3D bounding boxes, along with unlabeled 360-degree camera images, lidar, and bus data for three sequences. The dataset is hosted on AWS Open Data and is licensed under CC-BY-ND-4.0.
A rectified stereo video dataset from in vivo laparoscopic procedures, created by the Hamlyn Centre at Imperial College London. The repository contains rectified stereo images, calibration data, and ground truth, adapted for integration into the VSLAM-LAB framework. The dataset originates from the Endo-Depth-and-Motion project for 3D reconstruction and tracking in endoscopic videos.
Lidar data for Alaska was collected in 2004 using an OPTECH ALTM 70 kHz system mounted on a Cessna 320 aircraft by AeroMap for the NOAA Office for Coastal Management. The dataset represents last-return data and has been QA/QC'ed, though it may contain data holidays and diminished accuracy in areas of extreme terrain or dense vegetation. The data was last updated in the catalog on 2026-03 13.
UAV navigation data published on Kaggle, likely containing information relevant to autonomous flight and robotics. The dataset's specific columns, size, and collection details are not provided in the metadata. Its origin, author, and last update date are also unknown.
UAV_F32.dat is a dataset hosted on Kaggle. Its title suggests a focus on unmanned aerial vehicle data, likely containing telemetry or sensor readings. The dataset's specific content, size, and origin are not detailed in the provided metadata.
CS338_UAV123 is a dataset hosted on Kaggle. The title and platform tags suggest it contains video sequences captured from unmanned aerial vehicles (UAVs). It is likely intended for computer vision tasks such as object detection and tracking in aerial footage.
A dataset of labels for Unmanned Aerial Vehicle (UAV) imagery, published on Kaggle. The specific content, size, and creation details are unknown from the provided metadata. Its nature suggests it is intended for machine learning tasks involving aerial imagery analysis.