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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,023 datasets
David Farber's book 'Taken Hostage' provides a textual historical analysis of the 1979-1981 Iran Hostage Crisis. The work includes chapters covering the political context, key figures, the 444-day hostage period, and its aftermath. The description suggests the text is structured with an introduction, five chapters, an epilogue, notes, and an index.
A revised academic text examining the social and political forces shaping Saudi Arabia, including the impact of Islam and Westernization. The work by Fred Halliday draws heavily on Saudi sources and includes analysis of regional security dilemmas following the Gulf Wars. The dataset is a closed-license book sourced from the paperswithcode platform.
A historical dataset covering the development of Islamic art and architecture from the Umayyad period through the Ottomans. The data was compiled by author Robert Hillenbrand and is hosted on the paperswithcode platform. The description references specific dynasties and regions, including Syria, Iraq, Anatolia, and the Muslim West.
NuScenes Camera Part1 is a dataset hosted on Kaggle. The dataset likely contains camera sensor data from the NuScenes autonomous driving dataset. Specific details on the number of images, columns, and collection methodology are unavailable from the provided metadata.
Trajectory Prediction Beijing is a dataset of GPS trajectory data intended for destination prediction tasks. The dataset was published on Kaggle, but details about its author, organization, and creation date are unknown. The raw description indicates it contains GPS trajectory data for destination prediction, but specifics on volume, time range, and features are not provided.
A processed subset of the NuScenes dataset, which is a large-scale collection for autonomous driving. The original dataset contains data from multiple sensors, including cameras and LiDAR, collected in urban environments. This version has been processed, likely for machine learning tasks, and is hosted on Kaggle.
9424 km² of high-resolution LiDAR-derived Digital Terrain Model data covering land west of Exmouth in South West England. The dataset represents bare earth topography and is an outcome of the Centre for Ecology & Hydrology South West Project.
South West England is covered by a high-resolution LiDAR-based Digital Surface Model (DSM) dataset. It captures surface heights, including buildings and vegetation, across a 9424 km² area west of Exmouth. The dataset is part of the CEH South West Project outcomes.
Spectral reflectance measurements for quaking aspen trees include hyperspectral leaf and bark data from a handheld spectrometer and multispectral canopy data from airborne imaging. The dataset contains georeferenced samples with genotype and ploidy level information determined by DNA microsatellite analysis or flow cytometry. Data were collected at sites near Crested Butte in southwestern Colorado.
UK floodplains are covered by detailed Digital Elevation Models (DEMs) generated from Airborne LiDAR, owned by the Environment Agency and provided to the British Geological Survey for research. The data is produced from aircraft flying at 800 meters, capturing ground measurements at 2-meter intervals to create high-resolution terrain maps for flood risk assessment.
Tauhidul Islam is a dataset published on Kaggle. The dataset's specific content and structure are not detailed in the provided metadata. Its potential applications must be inferred from the title.
FlyingDrones is a synthetic benchmark dataset for optical flow estimation from unmanned aerial vehicle motion. The dataset was sourced from Kaggle, but its author, organization, and creation date are unknown. Its specific size, row count, and file formats are also unspecified.
A 10-frame sample from the FlyingDrones optical flow benchmark, which was presented at the NeurIPS 2026 conference. The dataset is intended for evaluating optical flow algorithms in drone-captured scenarios. Its full scope, including total frame count and specific collection details, is not provided in this subset description.
A training split for a drone detection dataset, likely containing aerial or ground-based imagery. The dataset is hosted on Kaggle, but its specific contents and creation details are not provided. The title suggests it is part one of a partitioned set intended for model training.
A dataset for drone detection, likely containing images or video frames. It is published on Kaggle and appears to be part of a larger collection split for machine learning training. The specific source, size, and creation date are unknown.
OG_SLAM is a dataset for robotics and autonomous navigation tasks, likely containing sensor data for Simultaneous Localization and Mapping (SLAM). It is published on Kaggle, but its specific contents, size, and creation details are not provided in the metadata. The dataset's actual structure, including its columns and sample data, is unknown.
Harita provides data from a drone mapping company specializing in LiDAR scanning and land surveying. The description mentions 380 optimized solutions for terrain measurement questions. The dataset's origin and specific collection details are not provided.
A dataset titled 'drone_dataset' published on Kaggle. The dataset's content likely relates to drone-captured imagery or sensor data, but specific details are unavailable. Further verification after download is required to confirm its size, features, and intended applications.
VisDrone_subset is a dataset published on Kaggle. The title suggests it is a subset of the VisDrone dataset, which is commonly used for computer vision tasks involving drone-captured imagery. The dataset's specific contents, size, and origin are not detailed in the provided metadata.
UAV-captured images likely depicting plastic contamination in an unspecified environment. The dataset is hosted on Kaggle, but its specific scale, collection date, and originating organization are unknown. Columns and sample data are unavailable, requiring verification after download.