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108 results for “imagery data”
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
Spartina alterniflora aboveground biomass patterns from Landsat 5 TM imagery (1984-2011) and external driver data used in multivariate analysis.
We used Landsat 5 TM satellite imagery to derive aboveground biomass estimates for the three height classes (tall, medium, short) of Spartina alterniflora on the Centeral Georgia Coast. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora aboveground biomass to landscape level estimates using 294 Landsat images acquired between 1984 to 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1,222 pixels covering 1.1 million m^2. Using univariate and linear multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. Drivers included monthly mean values for Altamaha River Discharge, Palmer Drought Severity Index, Standardized Precipitation Index, Mean Sea Level, Precipitation, and Temperature.
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.
<p>This submission provides the data and code for analyses reported in our publication.</p>
Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'
<p><strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong></p> <p><em>Running the Code</em></p> <p>SSL_Demo_2.m : main program for self-supervised machine learning segmentation</p> <p>SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m)</p> <p>This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background.</p> <p>It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure.</p> <p>Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run.</p> <p><em>Code Methodology</em></p> <p>The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required.</p> <p>Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination.</p> <p>The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy.</p> <p>Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions.</p> <p>The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): </p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a></p> <p>This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Caribou Poker Creek Research Watershed GIS Data: Spot 5 Imagery
Spot 5 pan-sharpened satellite image in the North Fairbanks area of the Tanana Valley, Alaska. This image was acquired and processed as part of the "Vegetation and Community Mapping of the Tanana Valley" project, conducted cooperatively by the State of Alaska Department of Natural Resources, Division of Forestry and Tanana Chiefs Conference. Originator: State of Alaska, Dept. of Nat. Resources, Division of Forestry. Publication_Date: 3/21/05. Geospatial_Data_Presentation_Form: remote-sensing image.
Bonanza Creek Experimental Forest GIS Data: Spot 5 Imagery
Spot 5 pan-sharpened satellite image in the Standard Creek area of the Tanana Valley, Alaska. This image was acquired and processed as part of the "Vegetation and Community Mapping of the Tanana Valley" project, conducted cooperatively by the State of Alaska Department of Natural Resources, Division of Forestry and Tanana Chiefs Conference. Originator: State of Alaska, Dept. of Nat. Resources, Division of Forestry. Publication_Date: 3/21/05. Geospatial_Data_Presentation_Form: remote-sensing image.
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Networks
<p>This multi-spectral satellite image data set is associated with our recent work on analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Neural Networks.</p> <p>The images were extracted using an automated Python script from Google Maps Static API, based on sample locations in four East African capital cities namely Kampala, Nairobi, Dar es Salaam, and Kigali.</p> <p>Other data sets associated with this work, that is, ESRI shapefiles for administrative level 1 and OpenStreetMap data for the named cities may be downloaded directly from the respective URLs provided in the manuscript.</p>
EEGlass motor-imagery and resting-state data
<p>Pilot acquisition of EEG data during motor-imagery and resting state (eyes-closed) from <a href="https://dl.acm.org/doi/10.1145/3341162.3348383">EEGlass eyeware prototype for ubiquitous brain-computer interaction.</a></p> <p>There are two types of EEG data: (1) motor imagery and (2) resting state during closed eyes from two EEG devices: (1) EEGlass through the OpenBCI board, and (2) Enobio 8 from Neuroelectrics. In addition, the EOG activity from four eye movements (up,down;left;right) from EEGlass are included. All datasets have been pre-processessed in EEGlab and exported as .set files.</p> <p><strong>Datasets:</strong></p> <ul> <li>Motor Imagery <ul> <li>EEGlass (data: MI_EEGlass.set; header: MI_EEGlass.fdt)</li> <li>Enobio (data: MI_Enobio.set; header: MI_Enobio.fdt)</li> </ul> </li> <li>Resting State (eyes-closed) <ul> <li>EEGlass (data: EC_EEGlass.set; header: EC_EEGlass.fdt)</li> <li>Enobio (data: EC_Enobio.set; header: EC_Enobio.fdt)</li> </ul> </li> <li>EOG <ul> <li>EEGlass <ul> <li> <p>EOG Up (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Down (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Left (EOG_U_EEGlass.set, .fdt)</p> </li> <li> <p>EOG Right (EOG_U_EEGlass.set, .fdt)</p> </li> </ul> </li> </ul> </li> </ul> <p><strong>Pre-processing:</strong></p> <ol> <li>Bandpass filtering: FIR 1-40 Hz</li> <li>Re-referencing: Common average reference (CAR)</li> <li>Channel locations <ul> <li>EEGlass [1:Nz; 2:TP9; 3:TP10]</li> <li>Enobio [1:Fpz ; 2:C3; 3:C4; 4:Pz]</li> </ul> </li> </ol> <p> </p> <p>Details from the pilot study can be found below:</p> <blockquote> <p>A. Vourvopoulos, E. Niforatos, M. Giannakos, 2019. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(UbiComp/ISWC '19 Adjunct). Association for Computing Machinery, New York, NY, USA, 647–652. DOI: https://doi.org/10.1145/3341162.3348383</p> </blockquote>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>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</p>
Code and data to support 'Street view imagery for built environment auditing: a systematic review'
<p>Code and data to support the manuscript entitled 'Street view imagery for built environment auditing: a systematic review'</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
<p>UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.</p>
Spekboom UAV imagery and reference data
<p>Dataset and products for the publication <strong>Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery</strong>.</p>
Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"
<p>open access data related to the paper "<strong>Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery</strong>" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology)</p> <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> <p>To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a></p>
Neurophysiologically meaningful motor imagery EEG simulated data
<p><strong>Description</strong></p> <p>This dataset contains different sessions of artificially generated motor imagery electroencephalographic (MI-EEG)-like signals. The data was generated based on PySimMIBCI [1], a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models.</p> <p>Specifically, MI was modeled by simulating an Event-Related Desynchronization (ERD) in the α band (9-11 Hz) over the contralateral motor area, representing the well-known brain dynamic during hand MI tasks. Right hand MI was differentiated from rest activity by a reduction in amplitude (ERD%) on the left hemisphere. For rest trials, idle α activity was simulated without ERD. The simulation includes three types of noise and artifacts: background noise, blink artifacts, and eye movement artifacts. For more information about the simulation process, please refer to [1].</p> <p>Different sessions were simulated, with each session representing different simulated scenarios:</p> <ol> <li> <p><strong>Self-Regulation Capabilities:</strong> To evaluate MI-BCI performance under different self-regulation capabilities, nine sessions were simulated with varying levels of ERD%, ranging from 50% to 10% in steps of 5%. These sessions reflect different user abilities to modulate α-band activity during MI, with higher ERD% indicating stronger MI modulation. The name of each session indicates the ERD% level, which was constant for all the trials of the session. For example, session <em>S_30</em> represents an ERD% of 30%. It is important to mention that in all the simulated sessions, the subject performed the task correctly in every trial, meaning no failed trials were included.</p> </li> <li> <p><strong>Varying MI Modulation:</strong> An MI vs. Rest session was generated where MI trials were simulated with an ERD% sampled from a uniform distribution from 10% to 50%. This session simulates trials with varying levels of MI modulation within the same BCI session, providing a more continuous representation of user performance fluctuations during MI-BCI tasks. The name of this session is <em>S_mix</em>.<strong> </strong>A list of the ERD% corresponding to each trial is included at the <em>S_mix_ERD_list.npy</em> file.</p> </li> <li> <p><strong>Fatigue-Related Artifacts: </strong>An additional MI vs. Rest session that shows the impact of non-MI-related cortical activity, specifically fatigue-related effects. Fatigue was simulated by increasing frontal θ and parietal α power across the course of the session, as described in [1]. The first half of the trials were fatigue-free, while the second half gradually incorporated increasing fatigue levels. The MI trials in this session were simulated with a fixed ERD% of 50% ensuring consistent modulation ability throughout the session. The name of this session is <em>S_fatigue</em>.</p> </li> </ol> <p>Each session contains 100 trials per class (MI vs. Rest), of 4s duration each, simulated at 41 electrodes following the 10-5 electrode system, with a sampling frequency of 1000 Hz. It is important to mention that in all simulated sessions, the task wask correctly performed in the 100% of the trials, meaning no failed trials were included.</p> <p><strong>File Format:</strong></p> <p>Epoched data are provided as <em>.fif </em>files. This data can be easily read by <em>mne.read_epochs</em> function from the MNE-Python library.</p> <p><strong>Potential Use Cases: </strong></p> <p>This dataset is suitable for testing hypothesis during MI-BCI algorithms development, specially to study the impact of subject self-regulation capabilities and non-MI-related cortical activity on BCI efficacy.</p> <p><strong>References:</strong></p> <p>[1] C. M. Galván, R. D. Spies, D. H. Milone and V. Peterson, "Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 2346-2355, 2024, doi: 10.1109/TNSRE.2024.3417311.</p>
Data for: A generalized area-based framework to quantify river mobility from remotely sensed imagery
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Data from: Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut
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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.