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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,662 datasets
OWLETS-2_SurfaceLidar_Data contains NASA TROPOZ and LMOL lidar measurements from the Ozone Water-Land Environmental Transition Study's second campaign. The dataset captures vertical profiles of ozone and wind over the Chesapeake Bay to study pollutant gradients across the land-water interface. Data were collected from June 6 to July 6, 2018, at sites including Hart Miller Island and the University of Maryland, Baltimore County.
OWLETS-1 deployed a unique combination of TOLNet ozone lidars, drones, and ship-based measurements to capture vertical profiles of pollutants over the Chesapeake Bay. The campaign specifically targeted the land-water transition zone from July 5 to August 3, 2017, with twelve intensive measurement days. This dataset provides synchronous vertical measurements of ozone and meteorology from two fixed sites—NASA LaRC (land) and the Chesapeake Bay Bridge Tunnel (water)—enabling the study of pollutant gradients.
OWLETS-1 Pandora Spectrometer Project data provides ozone and nitrogen dioxide measurements from a multi-site field campaign over the Chesapeake Bay. The dataset was collected from July 5 to August 3, 2017, using a combination of ground-based Pandora spectrometers, mobile, and ship-based platforms to study pollutant gradients across the land-water interface. It was supported by NASA's Science Innovation Fund to address measurement gaps in coastal air quality research.
Approximately 300 km offshore of San Francisco, Saildrone unmanned surface vehicles collected in-situ measurements during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot in October 2021 and an intensive operating period in Fall 2022. The dataset contains 5-minute averaged sensor data, including upper ocean currents, temperature, salinity, Chlorophyll-a fluorescence, dissolved oxygen, winds, air temperature, and surface radiation. It aims to understand how short-scale ocean dynamics influence vertical exchanges of physical and biological variables.
Historical records of the Annual Procurement Plans for the Municipal Mayor's Office of San José del Guaviare, Colombia. The dataset is published on the datos.gov.co platform and was last updated on 2026-05-18. It includes columns such as Consecutivo SECOP, Fecha de Publicación, Nombre del Documento, and Archivo Excel.
A multi-year passive acoustic monitoring dataset paired with LiDAR-derived forest structure metrics predicts Ruffed Grouse occurrence probability across Pennsylvania. The dataset, authored by Randy Koleck and last updated on 2026-04-22, was created to identify areas for targeted habitat management. The analysis indicates high-elevation, well-connected hardwood forests with some conifers and developed understories have the highest predicted probability of grouse occurrence.
LiDAR point cloud data captures forest canopy structure across key research sites in the Brazilian Amazon from 2008 to 2018. Collected for the Sustainable Landscapes Brazil Project, the data is georeferenced, noise-filtered, and provided in 1 km² tiles. The dataset is managed by ORNL_CLOUD and is available on multiple government data platforms.
An index of information classified as confidential or reserved by the Departmental Comptroller's Office of Guaviare, Colombia. The dataset includes columns for document titles, legal justifications, classification dates, and responsible officials. It was published on the Colombian open data portal and last updated in May 2026.
A geospatial dataset representing the coastline of Victoria, Australia, as of 2008. It was created by the Department of Energy, Environment and Climate Action, primarily using the zero-metre contour from LIDAR-derived elevation data and cross-referenced with high-resolution aerial photography. The dataset was last updated on the platform in April 2026.
Esquema de Informacion Alcaldia de San Jose del Guaviare is a catalog of public information published by the San Jose del Guaviare municipality in compliance with Colombian Law 1712 of 2014. The dataset is hosted on the Colombian open data portal www.datos.gov.co and was last updated on May 18, 2026. It lists information assets with details on their format, responsible parties, and update frequency.
Thermal-imaging drone surveys observed nocturnal spotlight surveys of white-tailed deer in Iowa, USA. The dataset includes one CSV file and two video clips from thermal drone surveys, totaling 610.4 MB. It was authored by David Delaney and last updated on May 6, 2026.
LiDAR data covering the Australian Capital Territory and Queanbeyan, collected in 2015. The dataset has a point density of 8 points per square meter in city centers and 4 points per square meter elsewhere, with a subset of full waveform data over Black Mountain. It is classified to Level 3 and delivered in LAS 1.4 format by the ACT Government Geospatial Data Catalogue.
Parameters for a bus-assisted heterogeneous-drone delivery model designed for rural e-commerce logistics. The dataset, created by Song Jin and published in 2026, is a small 5.5 KB Excel file containing the model inputs and performance metrics used in numerical experiments.
An accuracy assessment of spaceborne LiDAR data from the GEDI and ICESat-2 satellites for measuring water surface elevation. The study evaluates data across ten coastal bays on the Atlantic coast of the United States, showing ICESat-2 measurements have a root mean squared error of 0.08 m, while GEDI has 0.42 m but provides denser coverage. The dataset, authored by Adriana Parra and last updated in April 2026, is shared under a CC-BY-4.0 license.
This dataset contains Doppler lidar-derived vertical wind profiles collected during the FIRE-II cirrus cloud experiment in Kansas. It focuses on five priority days in November and December 1991, providing measurements from approximately 1.5 to 20.0 km above ground level. The data was produced by the NASA Langley Research Center Atmospheric Science Data Center to study cloud microphysics and radiative properties.
2012-2014 measurements from the Hurricane and Severe Storm Sentinel (HS3) campaign in the Atlantic Ocean basin. The dataset contains high-resolution Cloud Physics Lidar (CPL) data on cirrus clouds and aerosols, collected by NASA's Global Hawk aircraft to study storm formation and the Saharan Air Layer. Data is provided in netCDF/CF format by the GHRC DAAC.
G-LiHT's Digital Surface Model V001 provides LiDAR-derived visualizations of elevation above bare earth for terrestrial ecosystems. The data product is processed as multiple raster GeoTIFFs at a nominal 1-meter spatial resolution over locally defined areas across the Conterminous United States, Alaska, Puerto Rico, and Mexico. It is produced by NASA's Goddard Space Flight Center and was last updated on the platform in March 2026.
Polygon data delineates Water Framework Directive River Water Body Catchments and associated coastal catchments for areas draining directly to coastal waters. The dataset provides attribution for the 2022 classification cycle results and other relevant information for each water body. It was created by the UK Environment Agency using hydrological models based on EA LiDAR data and the Detailed River Network.
Polygon data delineates river water body catchments and coastal drainage areas for England and Wales. The dataset includes attribution from the 2019 Water Framework Directive classification cycle. It was created by the Environment Agency using hydrological models based on LiDAR and river network data.
Environment Agency polygon data for Water Framework Directive (WFD) River Water Body Catchments and associated coastal drainage areas. The dataset includes attribution from the 2019 classification cycle and has been geometrically simplified for web mapping. It was created using hydrological models based on EA LiDAR data and the Detailed River Network.