Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Migration Route of Swiss Ring Ouzels with Multi-Sensor Geolocator

This GeoLocator Datapackage contains the raw data for 5 multi-sensor geolocators and 4 light-level geolocators data equipped on Alpine Ring Ouzels (Turdus torquatus alpestris) in Switzerland between 2017-2020. The data has been processed using the GeoPressureR package to produce trajectories for the 5 multi-sensor tags. Code can be found on Github <a href="https://github.com/Rafnuss/migration-route-of-swiss-ring-ouzels">Rafnuss/migration-route-of-swiss-ring-ouzels</a>. The raw data has been used in <a href="https://doi.org/10.1111/jav.02860">10.1111/jav.02860</a>

opencc-by-4.0Sep 2022View details →

Multi-Sensor Ice Analysis Data: Analysis for Belgica Bank, North East Greenland 2019-20

The intention is that this dataset can be used for machine learning and deep neural network training/validation, and it distinguishes sea ice concentration, type and form derived from manual analysis of a combination of different satellite sensors including ALOS-2, Sentinel-1, COSMO-SkyMed, Sentinel-2, and ICESAT-2. The region chosen for the analysis was the Belgica Bank area offshore of North East Greenland, as this is an area which experiences a wide variety of sea ice, and iceberg, conditions throughout the year. The dataset consists of two parts: 11 days of individual sea ice interpretations, one for each month in the period from April 2019 to March 2020, with the exception of October 2019, and iceberg surveys derived from Sentinel-2 for spring in 2019 and 2020.      The dataset includes a user guide issued by MET Norway as report 10/2022 (see https://www.met.no/publikasjoner/met-report) in which the first part describes the data sources, nomenclature, file formats and data in the analysis. A second part of the report compares synthetic aperture radar (SAR) data from both L-band ALOS-2 and C-band Sentinel-1 satellites, and identifies the visible synergies and anomalies. The results confirm that there are variations in backscatter signatures between ALOS-2 and Sentinel-1 data when comparing them for different sea ice situations and conditions. ALOS-2 data in many cases is proven to be a reliable and beneficial source of data when it comes to identifying icebergs, ridges, determining sea ice type, and also distinguishing ice and water compared to standalone Sentinel-1 data.

opencc-by-4.0Jul 2024View details →

AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations

This datalake comprises several datasets from 37 networked low cost air quality multisensors (30 mobile ENEA MONICA(tm) + 7 fixed) along with 3 (fixed) + 1 (mobile) reference stations operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file. Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a pervasive, multi-site, multi-seasonal deployment in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy. The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with   deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. . Further description of sensors and reference instruments are reported in the accompanying paper (see citation request). The dataset can be used for  <ul> <li> advanced (remote/universal/in field) data driven calibration strategies test or development including machine learning models</li> <li>mobile opportunistic data fusion methods development</li> <li>geomatics and data assimilation models studies</li> </ul> as well as low cost sensor characterization performance studies.

opencc-by-4.0May 2023View details →

Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach

Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach". <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol>

opencc-by-4.0Feb 2023View details →

I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge

Dear Users, Data is password protected, to get password all you need to do is register using below link. Note that data is free of Cost  <a href="https://forms.gle/1gsVhJaJYT4mBp83A">Click here for Registration</a> Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. Consequently, the development of a Multilingual SV (MSV) system on the data collected in the Indian scenario is more challenging. With this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge 2022 has been designed for understanding and comparing the state of-the-art SV techniques. For the challenge, approximately 100 hours of data spoken by 100 speakers has been collected using 5 different sensors in 13 Indian languages. The data is divided into development, training, and testing sets and has been made publicly available for further research. The goal of this challenge is to make the SV system robust to language and sensor variations between enrollment and testing. In the challenge, participants were asked to develop the SV system in two scenarios, viz. constrained and unconstrained. The best system in the constrained and unconstrained scenario achieved a performance of 2.12% and 0.26% in terms of Equal Error Rate (EER), respectively.

opencc-by-4.0Mar 2023View details →

EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction

This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover. This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture. The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data. Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.

opencc-by-4.0Sep 2023View details →

Multi-Sensor Dataset From Android Smart Devices

This dataset contains data acquired on various Android devices, using an Android app called ''Mimir'', developed by the authors. Focus is given on raw GNSS measurements, but other sensors are also logged in the surveys. The dataset is provided under the CC-BY 4.0 license. More information are provided inside the ''readme.md'' provided along the dataset, as well as in our related publication.

opencc-by-4.0Sep 2023View details →

Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France

Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France.  * AGB and Volume maps are available on request. The methodology and validation of the maps are presented here: https://hal.science/hal-04249151 Please cite : David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151

opencc-by-4.0Sep 2024View details →

Data from multi-sensor devices and reference station to monitoring urban air quality

Data from electrochemical and optical sensors. <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td>Electrochemical sensor</td> <td>Optical sensor</td> <td>Probe</td> <td>Reference</td> </tr> <tr> <td> AE = auxiliary electrode (mV) WE = working electrode (mV) N = temperature correction  <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> PM1, PM2.5 and PM10 in µg/m³ </td> <td> Prs_mbar = pressure (mbar) Temp = temperature (°C) RH = relative humidity (%) </td> <td> DV30 = wind direction @ 30m (°) HR = relative humidity (%) NO, NO2, O3, PM10 and PM2.5 (µg/m³) Precipita = precipitation (mm) TC3 = temperature @ 3m (°C) VV30 = wind speed @ 30m (m/s) </td> </tr> </tbody> </table>

opencc-by-4.0May 2023View details →

Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks

We provide a multi-sensor dataset containing RGB-D and motion tracking data from sequential human-to-human object handovers. We recorded 12 pairs of participants executing shelving and un-shelving tasks involving 30 object handovers, resulting in 1440 handovers. Each recording consists of the position and orientation trajectories of 13 upper-body bones of the giver and the receiver and position trajectories of the 27 markers placed on their upper bodies, all recorded at 120Hz. The recordings also include two RGB-D data streams at 30Hz. We also provide four anthropometric measurements of the participants: height, waistline height, arm span, and weight. The dataset is valuable for investigating the body movements, grasps, and coordination strategies utilized by humans while performing tasks such as shelving which involve multiple sequential object handovers. Additionally, the dataset can be used to teach robots perform tasks involving object handovers with people, as well as self-handovers to correct grasps. The details about the dataset collection procedure are available in the article: Kshirsagar, A., Fortuna, R., Xie, Z., & Hoffman, G. (2025). Descriptor: Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks (MH2HO). IEEE Data Descriptions. (http://dx.doi.org/10.1109/IEEEDATA.2025.3580058)

opencc-by-4.0Jul 2023View details →

Global datasets to evaluate a multi-sensor approach for observation of floods

1. Overview This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review. 2. Dataset Descriptions There are two datasets included here. (a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination [GlobalMedianRevisits.zip] A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid. Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2  and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020. The following equation is used to calculate revisit periods: Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured) Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times. (b) Average revisit periods of satellites for flood records in the DFO database [FloodInfo.zip] Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database.

opencc-by-4.0Mar 2023View details →

Dataset For "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series"

This archive contains the input ant results files used with PickCraterSAR for publication "Nyiragongo crater collapses measured by multi-sensor SAR amplitude time series" submitted to JGR-SE. It also contains crops of each amplitude images used in this study in ENVI format with corresponding headers. At least, it contains the ash index values derives from SEVIRI data analysis.

dryad40/100

Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites

Open the record for dataset details and reuse information.

publicDec 2025View details →

opencc-by-4.0Aug 2020View details →

Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition

This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.   Equipment The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand. Hyperspectral data Hyperspectral data acquisition was performed with Surface Optics SOC710 camera.  This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range. The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel.  3D point clouds The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights  HD Images The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources.    Data The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles. Scene 1 (denoted SceneEagle) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand. Scene 2 (SceneFruit) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image. Scene 3 (SceneFruit2) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.   The data are formatted as follows: - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame. -  Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files. - Matched Hyperspectral clouds are also provided as \text{*.obj} files - HD photo files are provided in \text{*.jpg} files.   Acknowledgements This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.

opencc-by-4.0Feb 2021View details →

The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products, and the aggregate forecast statistics for the three real case studies

This is a data repository in support of the article "Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts. Part II: Assimilation Experiments of GOES-16 Satellite Derived Winds" submitted to AGU J. of Advances in Modeling of Earth Systems.  The data set consists of <ul> <li>The Multi-Radar Multi-Sensor (MRMS) and the Stage IV rainfall products used for validation in the three real case studies.</li> <li>The aggregate forecast statistics for composite reflectivity and APCP for the three real case studies are contained in the zipped files.</li> </ul>

opencc-by-4.0Jan 2024View details →

AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea

Context This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles. Content This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework. A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters. The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities. By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles. Technical Details <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page. Acknowledgements The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.