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Self-driving perception, LiDAR/camera fusion, trajectory prediction, drone perception, robot manipulation
1,663 datasets
NASA's CMS dataset provides 30-meter gridded estimates of aboveground biomass, canopy height, and canopy coverage for the state of Maryland in 2011. The models were built using 848 field sampling sites, leaf-off LiDAR data, and agricultural imagery, with predictions generated via random forests regression across three physiographic regions.
Iran, Islamic Republic data from the World Bank's portal, consolidated on HDX. The dataset likely contains indicators related to urbanization, traffic, congestion, and air pollution, sourced from organizations like the United Nations Population Division and World Health Organization. It was last updated on 2026-04-28 and is provided under a CC-BY-4.0 license.
World Bank Group data on environmental resources for Iran, Islamic Republic of. The dataset likely contains indicators covering forests, biodiversity, emissions, and pollution. It was last updated on 2026-04-28 and is available under a CC-BY-4.0 license.
Infrastructure data for Iran, Islamic Republic, compiled from sources like the International Road Federation and the International Telecommunications Union. The dataset is published by the World Bank Group under a CC-BY-4.0 license and was last updated on 2026-04-28. It contains indicators related to water, sanitation, energy, housing, transport, and information and communication technologies.
World Bank and UN data covering health systems, disease prevention, reproductive health, nutrition, and population dynamics for the Islamic Republic of Iran. The dataset aggregates indicators from sources including the United Nations Population Division, World Health Organization, UNICEF, and UNAIDS. It was last updated on 2026-04-28 and is provided under a CC-BY-4.0 license.
World Bank Group data on aid effectiveness for the Islamic Republic of Iran, last updated on 2026-04-28. The dataset covers indicators related to aid received and its impact on poverty reduction, growth, capacity building, and progress toward Millennium Development Goals. It includes measures of human welfare such as education and health outcomes.
A study by Guilherme Francio Niederauer, published on figshare in 2026, provides tabular data from UAV-based phenotyping of a Megathyrsus maximus (guinea grass) biparental population. The dataset likely contains results from optimizing image acquisition parameters, evaluating digital traits like pixel count and Haralick's entropy, and applying machine learning models to predict yield and canopy height. It includes analyses of ground sampling distance effects, heritability estimates, and genotype selection efficiency.
A study by Guilherme Francio Niederauer, published on figshare in April 2026, optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population. It examines how ground sampling distance, environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height, applying machine learning and mixed model analyses.
A simulated environment dataset from a study proposing an improved A* algorithm for underground mine transport robots. The dataset, shared by TongZhu Yu on figshare, compares search data between two algorithms to validate efficiency and safety improvements. It was last updated on 2026-05-04.
A dataset supporting research on improved path planning algorithms for underground transport robots in coal mines. The data was created by TongZhu Yu and published on figshare under a CC-BY-4.0 license in May 2026. It is a 72.3 MB archive containing simulation results for validating a hybrid A* and DWA algorithm.
CAMEX-3 Scanning Raman Lidar dataset provides vertical profiles of atmospheric constituents, specifically water vapor and aerosols. The data was collected by NASA during the CAMEX-3 campaign on Andros Island from 6 August to 20 September 1998. This dataset supports research into atmospheric composition and dynamics.
124 trials of Crown-of-Thorns starfish locomotion were analyzed using a Generalized Linear Mixed Model (GLMM). The table, authored by Masumi Kamata, summarizes statistical results including effect estimates, standard errors, confidence intervals, and p-values. It was last updated on May 5, 2026, and is shared under a CC-BY-4.0 license.
Ground-based measurements from the University of Utah Polarization Diversity LIDAR were collected at the CART site during the April-May 1996 SUCCESS Mission. This NASA field program involved over a hundred participants from multiple centers, agencies, and universities to study the effects of subsonic aircraft on contrails, cirrus clouds, and atmospheric chemistry. The dataset provides detailed observations from a focused, multi-institutional atmospheric campaign.
Southern Louisiana's Atchafalaya and Terrebonne Basins are covered by this dataset of UAVSAR Level 2 interferometric products from the 2021 Delta-X campaign. It provides water-level change observations from spring and fall deployments, with measurements taken at 30-minute intervals during flights. The data were collected by a NASA Gulfstream-III aircraft equipped with an L-band synthetic aperture radar.
L-band radar data from the Delta-X campaign provides water level change maps for the Atchafalaya and Terrebonne Basins in Louisiana. Three gridded products—temporalcoherence, waterlevelchange, and waterlevelchange_ramp—detail cumulative changes in centimeters at 30-minute intervals during spring and fall 2021 deployments. NASA's UAVSAR instrument on a Gulfstream-III aircraft collected these observations, which were validated against in-situ water level gauges.
NASA's UAVSAR L1 Single Look Complex stack products contain polarimetric L-band radar data collected during the Delta-X campaign over Louisiana's Atchafalaya Basin in spring and fall 2021. The dataset provides repeat-pass interferometric time series with 30-minute sampling intervals across HH, HV, VH, and VV polarizations, serving as the foundational data for deriving water level changes in wetlands. Data quality was validated against in-situ water level gauges deployed throughout the Mississippi River Delta floodplain.
Experimental simulation results comparing a proposed Vision-guided Multi-constraint RRT* (VM-RRT*) algorithm against the traditional RRT* algorithm for robotic arm path planning. The dataset, authored by Zhaopeng Yuan and last updated in April 2026, likely contains metrics such as planning time and end-effector motion parameters. The VM-RRT* algorithm achieved an average planning time of 3.27 seconds, approximately 20% faster than the traditional RRT*'s 4.07 seconds.
A 5.5 KB Excel file containing the standard Denavit-Hartenberg (DH) parameter table for an AUBO I5 robotic arm, uploaded by Zhaopeng Yuan in April 2026. The data is associated with research on a Vision-guided Multi-constraint RRT* algorithm for improving robotic grasping efficiency in chemical laboratory automation.
A 2017 raster elevation model covering over 60% of England at 1-meter spatial resolution. Produced by the Environment Agency, this Digital Surface Model (DSM) is derived from merged and re-sampled LIDAR archives, using the newest and best resolution data from repeat surveys. The dataset includes heights of objects like buildings and vegetation, has a vertical accuracy of +/-15cm RMSE, and is referenced to the Ordnance Survey Newlyn datum.
A 2-meter resolution raster elevation model covering approximately 75% of England, derived from airborne LIDAR surveys. Produced by the Environment Agency, the composite merges the best available data from a time-stamped archive and is updated annually. All LIDAR data has a vertical accuracy of +/-15cm RMSE and is referenced to the Ordnance Survey Newlyn datum.