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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
2,005 datasets
Twelve intensive measurement days from July 5 to August 3, 2017, captured ozone and pollutant gradients over the Chesapeake Bay. The Ozone Water-Land Environmental Transition Study (OWLETS-1) campaign used a Sherpa aircraft equipped with the GeoTASO instrument to collect trace gases, greenhouse gases, and navigational data. This dataset supports research on fundamental atmospheric processes at the challenging land-water interface.
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.
CI-VO results for sequence 10 in the KITTI dataset, likely containing performance metrics for a visual odometry algorithm. The data was contributed by Min Yue from Zhejiang University and is hosted on the Papers with Code platform. The specific metrics, data volume, and update recency are not detailed in the available metadata.
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.
OWLETS2_SurfaceLidar_Data contains ozone and wind lidar measurements from NASA's Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaign. The dataset was collected from June 6 to July 6, 2018 at sites including Hart Miller Island and the University of Maryland, Baltimore County in the upper Chesapeake Bay region. It provides synchronous vertical profiles of meteorology and pollutants to study land-water transition gradients.
Twelve intensive measurement days from July 5 to August 3, 2017, captured vertical ozone profiles over land and water. The OWLETS-1 campaign used TOLNet lidars, aircraft, drones, and ship-based instruments to measure pollutant gradients across the Chesapeake Bay. This dataset likely contains synchronous vertical measurements of meteorology and pollutants from two primary sites: NASA Langley Research Center and the Chesapeake Bay Bridge Tunnel.
NASA's OWLETS-1 field campaign collected ozone and nitrogen dioxide data over the Chesapeake Bay from July 5 to August 3, 2017. It utilized a unique combination of instruments, including Pandora spectrometers, aircraft, lidars, and ship-based measurements, to characterize pollutant gradients across the land-water interface. The dataset was designed to address the challenge of measuring air quality in coastal transition zones.
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.
2022 classification results for Water Framework Directive River Water Body Catchments in the UK. The dataset includes simplified polygon geometries for catchments and coastal drainage areas, created using hydrological models based on EA LiDAR data. It is published by the Environment Agency and updated in 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.