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Dataset results
59 results for “Multi-sensor”
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 2)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 1)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 3)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems
<p>Here we provide some example videos of safety violations of Apollo caused by sensor faults</p>
Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
ClinicalTrials.gov study NCT06380049. IPD Sharing: NO. Countries: 1. Publications: 0.
Acceptability, Feasibility, and Development of an AI Driven, Multi-sensor, Non-invasive Digital Mental Health Platform in Children With ADHD and Emotional Symptoms
ClinicalTrials.gov study NCT06311370. IPD Sharing: NO. Countries: 1. Publications: 0.
Nanowear Heart Failure Management Multi-sensor Algorithm
ClinicalTrials.gov study NCT03719079. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ML-Based Multi-Sensor Fall Risk Screening in DMD
ClinicalTrials.gov study NCT07015632. IPD Sharing: NO. Countries: 1. Publications: 0.
GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IPHEx
The GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IPHEx dataset consists of six different data products: precipitation rate, hourly rainfall accumulation, daily rainfall accumulation, hybrid scan reflectivity, three-dimensional reflectivity, and vertically integrated liquid content estimates. These data products were created using the NOAA NMQ System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RUC) model analysis fields, and Hydrometeorological Automated Data Systems (HADS) gauge data. The files provided in this dataset are from system output during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign that occurred in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. These data are available in ASCII and netCDF-4 formats for dates between April 30, 2014 through June 16, 2014.
Saildrone 2022 Arctic field campaign for the Multi-Sensor Improved SST (MISST) project
The Saildrone Arctic 2022 dataset presents a unique collection of high-quality, near real-time, multivariate surface ocean, and atmospheric observations obtained through the deployment of Saildrone, an innovative wind and solar-powered uncrewed surface vehicle (USV). Saildrone is capable of extended missions lasting up to 12 months, covering vast distances at typical speeds of 3-5 knots and operates autonomously, relying solely on wind propulsion, while its navigation can be remotely guided from land. The 2022 Saildrone Arctic campaign featured two Saildrone USVs deployed during a 60-day cruise in the Bering and Chukchi Seas, spanning from 18 June 2022 to 17 August 2022. The primary mission objective for 2022 was to gather comprehensive atmospheric and oceanographic data in Alaskan arctic waters, specifically in collaboration with the Distributed Biological Observatory (DBO; https://www.pmel.noaa.gov/dbo/; https://dbo.cbl.umces.edu/). Please see the cruise report: https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-docs/insitu/open/L2/saildrone/docs/Saildrone_2022_Arctic_Cruise_Report.pdf <p>During the Arctic campaign, Saildrones SD-1041 and SD-1046 undertook distinct trajectories to cover designated areas. SD-1041 traversed repeat transects from Point Hope, AK southwestward to near the International Date Line, following DBO line #3 (https://dbo.cbl.umces.edu/images/Frey_DBOmap_IceEdge2022.png). In contrast, SD-1046 ventured northward to DBO line #4 and, upon sea ice retreat, proceeded further north to DBO line #5. Each Saildrone was equipped with a suite of instruments to measure various parameters, including air temperature, relative humidity, barometric pressure, surface skin temperature, wind speed and direction, wave height and period, seawater temperature and salinity, chlorophyll fluorescence, and dissolved oxygen. Additionally, both vehicles utilized 300 kHz acoustic Doppler current profilers (ADCP) to measure near-surface currents. Seven temperature data loggers positioned vertically along the hull enhanced understanding of thermal variability near the ocean surface.<p>The Saildrone Arctic 2022 dataset, part of the Multi-sensor Improved Sea-Surface Temperature (MISST) project, encompasses three netCDF format files for each deployed Saildrone. The first file integrates saildrone platform telemetry and surface observational data at 1-minute temporal resolution including key parameters such as air temperature, sea surface skin, and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, and wind speed and direction. The second file focuses on ADCP current vector data, providing depth-resolved information to 100m at 2m intervals and binned temporally at 5-minute resolution. The third file includes temperature logger measurements at various depths at 1-minute resolution. This project, funded by NASA through the National Ocean Partnership Program (NOPP), demonstrates a commitment to advancing scientific understanding of the Arctic environment through innovative and autonomous observational technologies.
An Advanced Learning Framework for High Dimensional Multi-Sensor Remote Sensing Data Project
<p> Improve the use of land cover data by developing an advanced framework for robust classification using multi-source datasets:<br /> Develop, validate and optimize a generalized multi-kernel, active learning (MKL-AL) pattern recognition framework for multi-source data fusion.<br /> Develop both single- and ensemble-classifier versions (MKL-AL and Ensemble-MKL-AL) of the system.<br /> Utilize multi-source remotely sensed and in situ data to create land-cover classification and perform accuracy assessment with available labeled data; utilize first results to query new samples that, if inducted into the training of the system, will significantly improve classification performance and accuracy.<br /> &nbsp;</p>
Dataset for Multi-Sensor Observation of Production Line Operations
Open the record for dataset details and reuse information.
GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IFloodS V1
The GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IFloodS dataset contains quality control products, real time rain rate estimates, hourly precipitation rate estimates, and three-dimensional reflectivity products. These data products are also referred to as Multi-Radar Multi-Sensor Precipitation Reanalysis for Satellite Validation (MRMS) product and were created using the NOAA NMQ System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RUC) model analysis fields, and Hydrometeorological Automated Data Systems (HADS) gauge data. The files provided in this dataset are from the NMQ system output obtained during the GPM Iowa Flood Studies (IFloodS) field campaign that occurred throughout Iowa. These data are available in ASCII, netCDF-4, and binary formats for the dates between April 1, 2013 through June 30, 2013.
High Mountain Asia Gridded Glacier Thickness Change from Multi-Sensor DEMs V001
This data set contains gridded thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from KH-9 HEXAGON and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.
High Mountain Asia Glacier Thickness Change Mosaics from Multi-Sensor DEMs V001
This data set contains thickness change mosaics that include approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.
High Mountain Asia Average Glacier Thickness Change from Multi-Sensor DEMs V001
This data set contains average thickness changes for approximately 650 Himalayan glaciers between 1975 and 2000, and 1040 Himalayan glaciers between 2000 and 2016. The data were derived from HEXAGON KH-9 and ASTER digital elevation models (DEMs), by fitting robust linear trends to time series of elevation pixels over the glacier surfaces.
GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IPHEx V1
The GPM Ground Validation National Mosaic and Multi-Sensor QPE (NMQ) System IPHEx dataset consists of six different data products: precipitation rate, hourly rainfall accumulation, daily rainfall accumulation, hybrid scan reflectivity, three-dimensional reflectivity, and vertically integrated liquid content estimates. These data products were created using the NOAA NMQ System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RUC) model analysis fields, and Hydrometeorological Automated Data Systems (HADS) gauge data. The files provided in this dataset are from system output during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign that occurred in the Southern Appalachians, spanning into the Piedmont and Coastal Plain regions of North Carolina. These data are available in ASCII and netCDF-4 formats for dates between April 30, 2014 through June 16, 2014.
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Mesh)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p><span>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha</span><span>⁻</span><span>¹ with substantial spatial variability, ranging from 15.06 Mg C ha</span><span>⁻</span><span>¹ to 138.03 Mg C ha</span><span>⁻</span><span>¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha</span><span>⁻</span><span>¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.