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Robotics & Autonomous Systems

Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation

2,007 datasets

California Forest Biomass Estimates from Airborne LiDAR

California forests are analyzed in this dataset providing LiDAR-derived aboveground biomass estimates and uncertainty from 53 airborne surveys conducted between 2005 and 2014. The data was created by performing individual tree crown detection and applying a remote sensing aware allometric equation, with results available per-tree and in gridded formats. The dataset is provided by ORNL_CLOUD and includes the canopy height models used for the estimation.

GeospatialPoint CloudForest InventoryLidar SurveyCanopy Height ModelAboveground BiomassCalifornia+1

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Robotics & Autonomous Systems

Modelled Old-Growth Forest Extent in Victoria, Updated to 2018

A 2018-updated spatial dataset shows the modelled extent of Old Growth Forest in Victoria, Australia. It is based on the MOG2009 model, with reductions for disturbances from fire and timber harvesting up to July 2018. The dataset is provided by the Department of Energy, Environment and Climate Action and is licensed under CC-BY-4.0.

GeospatialPoint CloudForestryEnvironmental ModellingOld Growth Forest+1

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Robotics & Autonomous Systems

Individual Garry Oak Tree Crowns Mapped via LiDAR and Imagery in Greater Victoria

A 2026 study by Kate Mcintyre produced a map of individual Garry oak tree crowns across the Greater Victoria region. The dataset was created using an automated workflow that fused airborne laser scanning and four-band multispectral imagery to train a Random Forest classifier. The model achieved an overall accuracy of 89.7% and a specificity of 96.2%.

GeospatialPoint CloudEcologyMultispectral ImageryBenchmarkTree Mapping+1

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Robotics & Autonomous Systems

Individual Tree Segmentation from UAV-LiDAR Data, 2022–2025

Four years of UAV-LiDAR data (2022–2025) were used to evaluate four individual tree segmentation algorithms at a coastal Douglas-fir genetic trial site near Jordan River, Vancouver Island. The dataset includes normalized point clouds with a density of approximately 100 points/m² and performance metrics assessed against a field-referenced dataset of 1,526 live trees. The study was authored by Gayatri Deepak Kulkarni and harvested from Borealis Dataverse.

Time SeriesGeospatialPoint CloudForest PhenotypingTree SegmentationMulti Temporal AnalysisUav LidarComputer Vision+1

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Robotics & Autonomous Systems

Level Curves for Lévis, Québec from 2017 LiDAR Survey

Level curves for the territory of Lévis, Québec, with an equidistance of 50 cm. The data was produced from a LiDAR survey carried out in May 2017 and is provided by the Government and Municipalities of Québec. The dataset is available in multiple geospatial formats including FGDB, GeoJSON, and SHP.

GeospatialPoint CloudTopographyMunicipal Planning+1

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Robotics & Autonomous Systems

Widedepth: Indoor Fisheye Depth-Estimation Benchmark with LiDAR Ground Truth

An indoor fisheye depth-estimation benchmark dataset with millimeter-accurate ground-truth depth and disparity rendered from high-resolution LiDAR scans. It includes 101 indoor scenes captured with a fixed camera configuration of 195° FOV and 300 mm focal length. The dataset was created by Voxel51 and released on HuggingFace in May 2026.

ImageMultimodalFisheye ImageryDepth EstimationBenchmarkRoboticsComputer VisionIndoor Scenes+1

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Robotics & Autonomous Systems

Cv Ds All: Autonomous Driving Novel-View Synthesis Dataset

MHDCSM/cv_ds_all is a WebDataset-format collection of shards for autonomous driving novel-view-synthesis. The dataset includes training samples with ground-truth target images and separate test samples without targets. Each sample contains camera poses, intrinsics, timestamps, six camera views per timestamp, and dense world-frame point clouds.

ImagePoint CloudMultimodalMachine LearningData ScienceComputer VisionNovel View SynthesisAutonomous Driving+1

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Robotics & Autonomous Systems

WFD Surface Water Management Catchments: Cycle 3 Polygons

Surface Water Management Catchments are polygon boundaries defined for implementing the EU Water Framework Directive. The dataset represents Cycle 3 management units, delineated by aggregating smaller water body catchments using hydrological models based on LiDAR and expert judgement. These catchments are the geographic units for which formal WFD action plans are published.

GeospatialZIPWater managementSurface Water ManagementCatchmentWater Framework DirectiveHydrological Modeling+1

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Robotics & Autonomous Systems

WFD River Basin Districts Cycle 3: Water Management Reporting Units

River Basin Districts are polygon geospatial data delineating the largest reporting units for the European Water Framework Directive (WFD). The dataset, created using hydrological models based on Environment Agency LiDAR data, aggregates river catchments to ensure rivers do not intersect district boundaries and includes associated groundwaters and coastal waters. It specifically applies to Cycle 3 of the WFD reporting.

GeospatialZIPWater managementRiver Basin DistrictsGeospatial PolygonsWater Framework DirectiveHydrography+1

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Robotics & Autonomous Systems

CAMEX-4 Mobile Atmospheric Profiling System SODAR Data

This dataset contains wind and atmospheric data from the University of Alabama in Huntsville's Mobile Integrated Profiling System (MIPS), collected during the CAMEX-4 campaign. It provides high-temporal-resolution measurements from a Doppler Sodar, packaged as daily tar files containing 'cdf' files with 15-minute average 3-D wind profiles and 'mom' files with radial velocity and backscatter intensity for each beam. The data is hosted by the GHRC DAAC and appears on multiple authoritative Earth science platforms.

AudioTime SeriesMobile SensingBoundary LayerAtmospheric ProfilingWind ProfilesDoppler Sodar+1

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Robotics & Autonomous Systems

Ortho

A 2021 pilot survey commissioned for the NI 3D Coastal Survey captured bathymetric LiDAR data and simultaneous natural colour orthophotography. The data maps the nearshore areas of Dundrum Bay and parts of Carlingford Lough in Northern Ireland. It is provided by the Government Digital Service under the OGL-UK-3.0 license.

ImageGeospatialPoint CloudCSVJSONOrthophotographyCoastal SurveyBathymetry+1

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Robotics & Autonomous Systems

Bathymetric LiDAR And Orthophotography For Dundrum Bay And Carlingford Lough

A pilot bathymetric LiDAR survey commissioned in 2021 as part of the NI 3D Coastal Survey. The dataset maps the nearshore areas of Dundrum Bay and Carlingford Lough and includes simultaneously captured Natural Colour Orthophotography. It is provided by the Government Digital Service under an OGL-UK-3.0 license.

ImageGeospatialPoint CloudCSVJSONOrthophotographyComputer VisionCoastal SurveyBathymetry+1

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Robotics & Autonomous Systems

Footprint: 2021 Bathymetric LiDAR Survey of Dundrum Bay and Carlingford Lough

A pilot bathymetric LiDAR survey commissioned in 2021 as part of the NI 3D Coastal Survey. This dataset maps the nearshore areas of Dundrum Bay and Carlingford Lough, with natural colour orthophotography captured simultaneously. The data is provided by the Government Digital Service under the OGL-UK-3.0 license.

GeospatialPoint CloudCSVJSONOrthophotographyCoastal SurveyBathymetry+1

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Robotics & Autonomous Systems

VRI and LiDAR Forest Stand Metrics for Malcolm Knapp Research Forest, 2022

A 2022 study by Zijin Xiao compares provincial Vegetation Resource Inventory (VRI) stand metrics with LiDAR-derived metrics in the Malcolm Knapp Research Forest, British Columbia. The data includes stand metrics for crown density, height, and volume, and geophysical metrics like slope, elevation, and terrain wetness index. The analysis quantifies errors and maps their correlation with geophysical factors to guide forest data interpretation.

TabularGeospatialPoint CloudForest InventoryForestryGeospatial Analysis+1

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Robotics & Autonomous Systems

Terrestrial Lidar Scans of Forest-Tundra Ecotone in Alaska, 2016

NASA's ABoVE project provides terrestrial lidar scanning point cloud data from 10 research plots along the forest-tundra ecotone in Alaska's Brooks Range. Data were collected in June 2016 using a Leica ScanStation C10 laser instrument, with processed point spacing less than 1 centimeter. This high-resolution 3D data enables the derivation of ecological metrics for canopy structure and surface topography.

GeospatialPoint CloudAlaskaNasaForest Tundra EcotoneCanopy StructureEarth ScienceRemote Sensing+1

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Robotics & Autonomous Systems

20012026 Charging 03

A subset of the MolmoAct2-BimanualYAM dataset, a large-scale collection of bimanual robot manipulation demonstrations created using LeRobot. The full collection contains more than 720 hours of training demonstrations for diverse tabletop manipulation tasks. This subset includes annotated language instructions and was published by AllenAI in 2026.

MultimodalLanguage AnnotationsRobot ManipulationBimanual RoboticsLarge ScaleDemonstration Data+1

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Robotics & Autonomous Systems

10122025 Box 01

A subset of the MolmoAct2-BimanualYAM dataset, a large-scale collection for robot learning. It contains more than 720 hours of training demonstrations for diverse tabletop manipulation tasks. The dataset was created by AllenAI using LeRobot and includes annotated language instructions.

MultimodalLanguage AnnotationsRobot ManipulationBimanual RoboticsLarge ScaleDemonstration Data+1

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Robotics & Autonomous Systems

21012026 Charging 02

MultimodalRobot DemonstrationsLanguage AnnotationsBimanual ManipulationRoboticsLarge Scale+1

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Robotics & Autonomous Systems

18122025 Foldclo 01

720 hours of bimanual robot manipulation demonstrations collected for the MolmoAct2 project. This subset of the MolmoAct2-BimanualYAM dataset includes annotated language instructions. The dataset was created by AllenAI using LeRobot and was last updated in May 2026.

MultimodalLanguage AnnotationsBimanual ManipulationRoboticsLarge ScaleDemonstration Data+1

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Robotics & Autonomous Systems

LiDAR Point Cloud and DEM for Southern Idaho Watersheds

August 2014 LiDAR point cloud data covers the Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The dataset includes georeferenced, noise-filtered point clouds provided in 1 km tiles, alongside derived high-resolution digital elevation models and maximum vegetation height maps. Data originates from the ORNL_CLOUD organization.

GeospatialPoint CloudVegetation HeightLidar Point CloudWatershed ResearchDigital Elevation Model+1

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