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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,671 datasets
Lidar data classified into nine distinct categories, including ground, water surface, and submerged objects, in accordance with ASPRS standards. The National Oceanic and Atmospheric Administration collected this data via airplane using a Leica Hawkeye4X system. Acquisition occurred from November 3, 2022, through February 5, 2023, covering the area from Bethany Beach to Chincoteague, Maryland.
2011 NOAA National Geodetic Survey lidar data collected for Fire Island, New York, and Cape May to Absecon Inlet, New Jersey. The data includes topographic point clouds in LAS 1.2 format, classified as unclassified, ground, and water, along with lidar intensity and encoded RGB values. Fire Island data was collected on November 25, 2011, and the New Jersey data was collected on July 16, 2011.
Level-2 geolocated surface elevation and canopy height data were collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite. The dataset provides processed measurements from the LVIS instrument. The data provider is the NSIDC_CPRD organization.
LVIS Classic L2 Geolocated Surface Elevation and Canopy Height Product V001 contains Level-2 geolocated surface elevation and canopy height measurements. Data was collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite. The dataset is provided by the NSIDC_CPRD organization.
UAV-OBB is a dataset for vehicle detection collected from drones. The dataset likely contains aerial imagery intended for smart city traffic monitoring applications. Its author, organization, and specific size are unknown.
A dataset for detecting airplanes, likely using imagery from drones and potentially a source named Baird. It is hosted on the Kaggle platform. The specific scale, collection method, and time period are not detailed in the available metadata.
MUUFL Gulfport is a campus-scale dataset containing co-registered hyperspectral and LiDAR data. The data is labeled for 11 urban land cover classes. The dataset was sourced from Kaggle, but the author, organization, and specific collection details are not provided.
Orthophotography was captured as part of the 2021 NI 3D Coastal Survey. Coverage extends across the entire Northern Ireland coastline, including the intertidal area, and extending approximately 200 meters landward of the high water mark. Colour Infrared orthophotography imagery was captured with a 10cm resolution.
Four high-resolution RGB unmanned aerial vehicle images capture European beech forests in Germany's RhÓ§n Biosphere Reserve from September 2020. The dataset, with approximately 3cm pixel size and an associated 10cm digital elevation model, was collected to analyze individual tree and stand-level canopy damage following an extreme 2018/2019 drought. The work was supported by the Natural Environment Research Council.
2026-maintained geospatial data from the City of Austin provides continuous stream centerlines derived from aerial orthophotography, LIDAR-based planimetrics, and construction plans. The layer includes a maintenance version and features updated attribute domains for classifying altered channels.
drone_detection is a dataset hosted on Kaggle. The title suggests it contains imagery or sensor data for identifying drones. The dataset's specific content, size, and origin are not detailed in the provided metadata.
KITTI_det_reid_depth is a dataset from Kaggle, likely derived from the KITTI Vision Benchmark Suite. The dataset appears to combine tasks of object detection, person re-identification, and depth estimation, which are common in computer vision for autonomous systems. Its specific contents, scale, and origin require verification after download.
Judith Miller's first-hand account reports on militant Islamic movements across ten Middle Eastern countries, including Egypt, Saudi Arabia, Sudan, Algeria, Libya, Lebanon, Syria, Jordan, Israel, and Iran. The work is based on twenty years of reporting experience in the region. It analyzes the struggle between modernity and militant Islam reshaping the Middle East.
Kaggle hosts a dataset focused on drone detection. The platform tags indicate it contains aerial imagery for object detection tasks. Metadata is minimal; actual content requires verification after download.
A dataset likely containing simulated data for Unmanned Aerial Vehicles (UAVs), published on the Hugging Face platform. It was uploaded by the author 'wraphierdz' and was last updated on April 14, 2026. The specific contents, scale, and collection method are not detailed in the available metadata.
Lane detection sample images published on Kaggle. The dataset likely contains visual data of road scenes intended for computer vision tasks. Metadata is minimal; actual content, scale, and collection details require verification after download.
Lane detection test images likely intended for validating computer vision models in autonomous driving contexts. The dataset is hosted on Kaggle, but its creator, size, and specific contents are unspecified. Its primary purpose appears to be evaluating algorithms for road lane identification.
UAV Mobility Prediction is a dataset hosted on Kaggle. Its specific content and scale are not detailed in the available metadata. The dataset likely contains information for predicting the movement of unmanned aerial vehicles.
Indonesian Islam under the Japanese Occupation, 1942-1945 is a historical text by Harry J. Benda of the RAND Corporation, first published in 1958. It provides a systematic political history of Indonesian Islam, analyzing Muslim reactions to Dutch policies and the community's rise during the Japanese interregnum. The book is described as an authoritative guide to modern Indonesian history.
guava_fruit is a dataset hosted on Kaggle. The dataset likely contains information related to guava fruit, such as characteristics, quality metrics, or cultivation data. Its specific content, size, and origin require verification after download.