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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,023 datasets
UAVSAR_INSAR_INTERFEROGRAM_GRD is a dataset of ground-projected scenes from NASA's UAVSAR platform using repeat-pass interferometry. The data is hosted by the Alaska Satellite Facility (ASF) on the NASA Earthdata platform. The specific temporal and spatial coverage, file formats, and data volume are not detailed in the provided metadata.
UAVSAR repeat pass interferometry scenes provide ground-projected amplitude data for geophysical analysis. The data is hosted by NASA Earthdata and originates from the Alaska Satellite Facility (ASF). The specific temporal and geographic coverage, row count, and file formats are not detailed in the available metadata.
NASA airborne campaigns collected this set of geotagged images using Digital Mapping Cameras mounted alongside the Land, Vegetation, and Ice Sensor (LVIS) lidar altimeter. The data is provided by the NSIDC_CPRD organization. The exact temporal coverage, geographic scope, and volume of images are not specified in the provided metadata.
A processed version of the nuScenes dataset, a key benchmark for autonomous driving. The title suggests it may contain sensor data like images and LiDAR point clouds, aggregated or downsampled with a stride of 4. It is hosted on Kaggle, but the specific processing steps and content are not detailed in the available metadata.
From August 1991 to March 1994, the Raman Lidar Database contains data from approximately 150 measurement nights, with about 5 nights per month, recorded in Geesthacht. For each event, it provides aerosol absorption and extinction coefficients between 3 km and 30 km altitude, with a maximum height resolution of about 60 meters. The dataset is hosted on NASA EarthData and was last updated in March 1994.
A dataset titled 'tinyperson_visdrone_widerperson' is hosted on Kaggle. The title suggests it is likely a benchmark collection for object detection, potentially combining or relating to the TinyPerson, VisDrone, and WiderPerson datasets. Its specific content, size, and creation details are not provided in the available metadata.
UAV-SEAD provides real-world multivariate time-series telemetry for state estimation anomaly detection in Unmanned Aerial Vehicles (UAVs), released by Aykut Kabaoglu and Sanem Sariel in 2026. The data supports research into Fault Detection and Identification (FDI) specifically for PX4-based flight systems.
Malaysian Borneo canopy height data contains 36,655 repeated measurements from airborne LiDAR before and after the 2015-16 El Niño event. The dataset includes coordinates for spatial analysis and features like Topographic Position Index and distance from oil palm plantations to study environmental effects. It was collected in November 2014 and April 2016 across a human-modified tropical landscape.
13 January 2023 terrestrial LiDAR scans capture two overlapping 3D point clouds for a nearly 18,000 m² wooded area in Nottinghamshire, UK. The dataset provides forest structure measurements for trees infested with common ivy (Hedera helix) and a non-infested sample, collected for a study on associated soil organisms.
A model of woody linear features on field boundaries in England, derived from Environment Agency lidar captured between 2016 and 2021. The dataset maps the extent and height class of hedgerows, tree lines, and thickets, excluding urban areas, woodlands, open water, and mountain terrain.
78,000 vegetation outlines and tree tops above 1 meter in height, processed from LiDAR data. The data were created for Cornwall and Devon as part of the Tellus South West project during July and August 2013.
2010 global raster datasets of forest biomass, comprising four layers: growing stock volume (GSV), above-ground biomass (AGB), and their respective per-pixel uncertainty estimates. The data was produced by Maurizio Santoro using spaceborne SAR, optical, LiDAR, and auxiliary datasets with multiple estimation procedures. GSV and AGB data are available as 40-degree by 40-degree tiles.
LiDAR geospatial data were collected on approximately 60,000 acres of White River National Wildlife Refuge in late fall-winter 2016-17. The data, provided by the Department of the Interior, are delivered as geo-referenced files in zipped folders. Associated metadata is described as fully compliant.
An aerial imagery dataset focused on detecting vehicles in urban environments. The dataset is described as high-precision and is hosted on Kaggle. The author, organization, and specific collection details are unknown.
UAV_DETECTION_B is a dataset for detecting unmanned aerial vehicles, likely containing images or video frames. It is published on Kaggle, a platform for data science competitions and projects. The specific collection method, time range, and author are not detailed in the available metadata.
UAV_INST_DATA is a dataset of 29,888 UAV (Unmanned Aerial Vehicle) image tiles with instance segmentation annotations in COCO format. It contains 114,876 annotations across 3 categories, derived from 3,802 source images. The dataset was uploaded by jeff-calderon to Hugging Face and was last updated in February 2026.
Kaggle hosts a dataset titled 'UAV_Dataset'. The dataset likely contains imagery or sensor data captured by Unmanned Aerial Vehicles. Its specific content, size, and creation details are not provided in the available metadata.
A subset of the nuScenes dataset, which is a large-scale collection for autonomous driving research. It is published on Kaggle, but the specific size, scope, and creation details of this subset are unknown. The original nuScenes dataset was created by Motional.
OpenDataNI commissioned a pilot bathymetric LiDAR survey in 2021 as part of the NI 3D Coastal Survey. The dataset maps the nearshore areas of Dundrum Bay and Carlingford Lough, with natural colour orthophotography captured simultaneously. The data is available in HTML and JSON formats under the OGL-UK-3.0 license.
Guava123 is a dataset published on Kaggle. The title suggests it likely contains information related to guava fruit or agriculture. The dataset's specific content, size, and origin are not detailed in the available metadata.