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1,025 results for “vision”
AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots
<p>This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, <strong>images.zip</strong>, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process.</p> <p>The second archive, <strong>videos_app_HR.zip</strong>, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization.</p> <p>The third archive, <strong>videos_app_TM.zip</strong>, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers.</p> <p>In addition to the visual data, the dataset includes a comma-separated values (CSV) file named <strong>information_all_trees.csv</strong>, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations.</p> <p>The column headers in <strong>information_all_trees.csv</strong> are as follows:</p> <ul> <li><strong>PLOT_ID</strong>: The numeric identifier for each sample plot.</li> <li><strong>tree_species_short</strong>: Abbreviation of the tree species.</li> <li><strong>caliper_dbh</strong>: The manually measured dbh of the tree in centimeters.</li> <li><strong>AVP_App1_dbh</strong>: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li><strong>AVP_App2_dbh</strong>: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li><strong>res_App1</strong>: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>res_App2</strong>: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>tree_species</strong>: The Latin name of the tree species, with genus and species connected by an "_".</li> <li><strong>tree_class</strong>: Classification of the tree into a species-specific category.</li> <li><strong>date</strong>: The date of the recordings.</li> <li><strong>time_App_1_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li><strong>time_App_2_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li><strong>time_manual_caliper_min</strong>: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li><strong>measuring_person</strong>: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li><strong>mean_slope_degrees</strong>: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> <p>This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.</p>
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
The European Energy Vision 2060 (EU EnVis-2060): Scenario Parametrization
<h3>Description</h3> <p>This repository contains the scenario parametrization for the European Energy Vision 2060 (EU EnVis-2060) scenarios, which have been created by the European research projects Man0EUvRE (funded by the CETPartnership) and iDesignRES (funded by the European Commission). The data is formatted in the IAMC data format (see <a href="https://pyam-iamc.readthedocs.io/en/stable/data.html">https://pyam-iamc.readthedocs.io/en/stable/data.html</a>). </p> <p>The underlying raw data, including all sources and assumptions used for each data point can be found at the Global Energy System Model (GENeSYS-MOD) data repository (see <a href="https://github.com/GENeSYS-MOD/GENeSYS_MOD.data">https://github.com/GENeSYS-MOD/GENeSYS_MOD.data</a>). </p> <p> </p> <p>Alongside the scenario parametrization, there is also included a short report about the qualitative storylines, the workflow, and some key assumptions as part of Deliverable 1.2 of the Man0EUvRE project, as well as the Q2Q (qualitative to quantitative) matrix used in the process of the parametrization.</p> <p> </p> <h3>Changelog</h3> <table> <tbody> <tr> <td>Version</td> <td>Date</td> <td>Changes</td> </tr> <tr> <td>3.1</td> <td>08.09.2025</td> <td> <p>Improvements in district heating, technology costs for wind, PV, and electrolyzers. Updated fossil fuel import prices.</p> </td> </tr> <tr> <td>3.0</td> <td>31.07.2025</td> <td> <p>Further refinement of data set, used for <a href="https://doi.org/10.5281/zenodo.16640689">quantification</a> of the scenarios with GENeSYS-MOD (v1.1.0)</p> <p>Data changes are based on partner feedback and further calibration for the European scenarios.</p> </td> </tr> <tr> <td>2.0.1</td> <td>11.03.2025</td> <td> <p>Added newest version of Q2Q matrix</p> </td> </tr> <tr> <td>2.0</td> <td>28.02.2025</td> <td> <p>Significantly overhauled data set, used for <a href="https://doi.org/10.5281/zenodo.14959447">quantification</a> of the scenarios with GENeSYS-MOD (v1.0.1)</p> </td> </tr> <tr> <td>1.0.2</td> <td>11.09.2024</td> <td>Fixed missing hydropower data in capacities due to an error in the conversion script</td> </tr> <tr> <td>1.0.1</td> <td>07.09.2024</td> <td>Fixed missing data in residual capacities</td> </tr> <tr> <td>1.0</td> <td>06.09.2024</td> <td>Initial Upload</td> </tr> </tbody> </table> <p> </p> <h3>Funding</h3> <p>This research was funded by CETPartnership, the European Partnership under Joint Call 2022 for research proposals, co-funded by the European Commission (GA N°101069750) and with the funding organisations listed on the CETPartnership website.</p>
Super-resolving ocean dynamics from space with computer vision algorithms: training datasets
<p>We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in <em>Buongiorno Nardelli et al.</em> (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [<em>Ciani et al.</em>, 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [<em>Clementi et al. 2021</em>]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions (this step is achieved by running the SWOT simulator software [<em>Gaultier et al.</em>, 2016]) and successively applying the DUACS (<em>Data Unification and Altimeter Combination System)</em> mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper:</p> <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. <em>Remote Sens.</em>, <strong>2022</strong>, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>
Bridging the gap between single nanoparticle imaging and global electrochemical response by correlative microscopy assisted by machine vision
<p>The data in this repository corresponds to experimental data: linear sweep voltammetry, optical movie and the database of the SEM images. They support the findings of a study discussed in the article by Godeffroy et al. published in Small Methods with the doi: http:/doi.org/10.1002/smtd.202200659. The data analysis to reproduce the results presented in the article has been carried out by homemade Python program routines also provided in this repository. The descirption of each routine is also provided in a text file.</p>
Extended datasets from MM-IMDB and Ads-Parallelity dataset with the features from Google Cloud Vision API
<p>This is extended datasets from MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] dataset with the features from Google Cloud Vision API. These datasets are stored in jsonl (JSON Lines) format.</p> <p><strong>Abstract (from our paper):</strong></p> <p>There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM<sup>2</sup>S<sup>2</sup>). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.</p> <p><strong>Dataset (MM-IMDB and Ads-Parallelity):</strong></p> <p>We extended two multimodal datasets, namely, MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] for the empirical experiments. The MM-IMDB dataset contains 25,925 movies with multiple labels (genres). We used the original split provided in the dataset and reported the F1 scores (micro, macro, and samples) of the test set. The Ads-Parallelity dataset contains 670 images and slogans from persuasive advertisements to understand the implicit relationship (parallel and non-parallel) between these two modalities. A binary classification task is used to predict whether the text and image in the same ad convey the same message.</p> <p>We transformed the following multimodal information (i.e., visual, textual, and categorical data) into textual tokens and fed these into our proposed model. We used the <a href="https://cloud.google.com/vision">Google Cloud Vision API</a> for the visual features to obtain the following four pieces of information as tokens: (1) text from the OCR, (2) category labels from the label detection, (3) object tags from the object detection, and (4) the number of faces from the facial detection. We input the labels and object detection results as a sequence in order of confidence, as obtained from the API. We describe the visual, textual, and categorical features of each dataset below.</p> <p><em><strong>MM-IMDB</strong></em>: We used the title and plot of movies as the textual features, and the aforementioned API results based on poster images as visual features.</p> <p><em><strong>Ads-Parallelity</strong></em>: We used the same API-based visual features as in MM-IMDB. Furthermore, we used textual and categorical features consisting of textual inputs of transcriptions and messages, and categorical inputs of natural and text concrete images.</p>
Dynamic reconfiguration of macaque brain networks during natural vision
<p>Raw data acquired under awake imaging conditions in the macaque monkey during free-viewing on natural scenes. The movie presented is also shared which is based on 30 sec 0N and OFF periods. Time-series echo planar imaging (EPI) data for each subject (AL, DP, FL and VL). Data is named based on the subject.session.run. The format structure is on NIFTI. Anatomical files are also named according to the same nomenclature. EPI mask are also available for each in-session subject. </p>
Image Databases for Computer Vision Coded for Subject Traceability
<p>This document consists of the corpus of image databases examined for traceability of dataset subjects as published in:</p> <p>Morgan Klaus Scheuerman, Katy Weathington, Tarun Mugunthan, Emily Denton, and Casey Fiesler. 2023. From Human to Data to Dataset: Mapping the Traceability of Human Subjects in Computer Vision Datasets. Proc. ACM Hum.-Comput. Interact. 7, CSCW1, Article 55 (April 2023), 33 pages. https://doi.org/10.1145/3579488</p>
MCR LTER: Coral Reef: Computer Vision: Moorea Labeled Corals
The Moorea Labeled Corals dataset is a subset of the MCR LTER packaged for computer vision research. It contains 2055 images from three habitats IDs: fringing reef outer 10m and outer 17m, from 2008, 2009 and 2010. It also contains random point annotation (row, col, label) for the nine most abundant labels, four non coral labels: (1) Crustose Coralline Algae (CCA), (2) Turf algae, (3) Macroalgae and (4) Sand, and five coral genera: (5) Acropora, (6) Pavona, (7) Montipora, (8) Pocillopora, and (9) Porites. These nine classes account for 96% of the annotations and total to almost 400,000 points. These nine classes are the ones analyzed in (Beijbom, 2012); less-abundant genera not treated in the automation are also present in the dataset. These data were published in Beijbom O., Edmunds P.J., Kline D.I., Mitchell G.B., Kriegman D., 'Automated Annotation of Coral Reef Survey Images', IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, 2012. [BibTex] [pdf] These data are a subset of the raw data from which knb-lter-mcr.4 is derived. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
High-throughput robotic titration using computer vision
<ul> <li> <p>An automated HTE robotic titration using a liquid-handling robot Opentrons(OT-2) and a standard webcam enables in-situ, affordable titration analyses.</p> </li> <li>Its modular design allows adaptability for materials chemsitry and integration into automated workflows, enhancing efficiency in chemical search.</li> </ul>
Data to Three-Dimensional Binocular Eye-Hand Coordination in Normal Vision and with Simulated Visual Impairment
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Kwon, M. & Bex, P.J. (2018) Three-dimensional binocular eye--hand coordination in normal vision and with simulated visual impairment. <em>Experimental Brain Research</em>. https://doi.org/10.1007/s00221-017-5160-8</p>
Data on eye movements in people with glaucoma and peers with normal vision
<p>Eye movements were recorded from 44 elderly glaucoma patients and 32 age-similar healthy vision controls whilst watching three separate small video clips.</p>
Deliverable 1.2: Collection of national reports on the citizens' future visions
<p>This deliverable presents the 30 national reports on the citizens’ future visions from the National Citizen Vision Workshops (NCVs), held as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research & Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for a desirable and sustainable future.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV citizens met for a full day to formulate and debate their visions for desirable and sustainable futures. All together 179 visions were formulated during the NCVs by more than 1000 citizens.</p> <p>The national reports on the citizens’ future visions each includes a summary of the NCV process and presents the original and unedited visions (raw visions and six<a href="#_ftn1">[1]</a> final visions) for each country. In addition, all reports include information on participant data. The summaries and the final visions are to be found in the national language and translated into English.</p> <p>The national reports on the citizens’ future visions offer a unique opportunity to identify the European citizens’ wishes, needs and demands for a sustainable and desirable future. The reports may inspire and give input to experts, policy- and decision makers all over Europe, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>In the following, we introduce the necessary background information on how to read the national reports and interpret the visions. In addition we give a resume of the methodology and the NCV process. A deeper analysis of the results is to be found in Deliverable 1.3 –Vision Catalogue and Devliverable 2.1 –First draft of the societal needs research programme scenarios. </p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project began in June 2015.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> Ireland is an exception, since this country only formulated 5 visions.</p>
Deliverable 1.3 -Vision Catalogue - Encompassing the visions from all 30 countries
<p>This deliverable presents an English translation of the 179 visions elaborated by more than 1000 citizens during the National Citizen Vision Workshops (NCVs), arranged as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research and Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for desirable and sustainable futures.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV 25-42 (36 on average) citizens met for a full day to formulate and debate their visions for a desirable and sustainable future.</p> <p>The visions represent the final product of the NCVs and are the result of a comprehensive and intensive vision building process in each of the participating countries. The visions were originally formulated in the citizens’ national language, but for simplicity all visions have been translated into English. The original visions and national reports from each NCV are to be found elsewhere (Deliverable 1.2 - Collection of national reports on the citizens’ future visions).</p> <p>The present deliverable documents the European citizens’ wishes, needs and demands for a desirable future. The visions enable dialogue between the citizens and the European policy- and decision makers, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project was kicked-off in June 2015</p> <p> </p>
EOL computer vision pipelines: Classification for Image Tagging: Flower Fruit
<p>Angiosperms: Stats from Colab:</p> <ul> <li>Number of positive identified reproductive structures: 490</li> <li>Number of possible identified reproductive structures: 4611</li> <li>Number of negative identified reproductive structures: 14833</li> </ul> <p> </p>
EOL computer vision pipelines: Classification for Image Tagging: Image Type: Anura
<p>Produced by the EOL Image Type Classifier. Classifies images as map, phylogeny, illustration, herbarium sheet, or none. Dataset generated for EOL Anura images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-vs-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
EOL computer vision pipelines: Classification for Image Tagging: Image Rating: Chiroptera
<p>Produced by the EOL Image Rating Classifier. Classifies images as bad or good quality (used for image gallery sorting). Dataset generated for EOL Chiroptera images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-or-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.</p>
SEENIC: dataset for Spacecraft posE Estimation with NeuromorphIC vision
<p>Dataset used in the paper "Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing" (<a href="https://doi.org/10.48550/arXiv.2209.11945">arXiv</a>, <a href="https://ieeexplore.ieee.org/document/10160531">IEEE Xplore</a>), for the purpose of satellite pose estimation with an event camera.</p> <p>Both events and ground truth camera poses were captured across the 20 scenes in total. There are two trajectories, five lighting configurations and two camera speeds. All combinations of trajectory type, speed and lighting configuration were enumerated for capture. Sample event frames and dataset statistics are available in the paper linked above, along with our pose estimation method used on this dataset.</p> <p> </p> <p>Live-capture scene names use the following encoding: {satellite model}-{trajectory}-{speed}-{lighting configuration}</p> <p>The calibration scene (calibration.tar.gz) includes multiple views of a chessboard used to calibrate the camera intrinsics and extrinsics for the live-capture scenes. Camera parameters calibrated using this scene can be found in the <strong>calib.txt</strong> file, with the format: fx fy cx cy k1 k2 p1 p2 k3.</p> <p> </p> <p>All <strong>live-capture</strong> scenes have the same data format:</p> <p>scene/</p> <p> poses/ -- Raw timestamped robot gripper to base transforms</p> <p> cam-poses.csv -- Ground truth camera poses with the format {timestamp, Rx, Ry, Rz, x, y, z}</p> <p> events.csv -- Event stream with the format {timestamp, x, y, polarity (0=off, 1=on)}</p> <p> meta.json -- Metadata file with camera frame dimensions</p> <p>Note: all timestamps are in microseconds.</p> <p> </p> <p>The <strong>synthetic</strong> scene (synthetic.tar.gz) has the following data format:</p> <p>synthetic/</p> <p> poses/ -- Sequential poses captured at a constant time interval</p> <p> events.txt -- Event stream with the format: time (float s), x, y, polarity (0=off, 1=on) as specified at <a href="https://rpg.ifi.uzh.ch/davis_data.html">https://rpg.ifi.uzh.ch/davis_data.html</a></p> <p> camera_intrinsics.txt -- The camera intrinsic matrix (space separated)</p> <p>Note: please refer to the paper referenced below for further details on using this synthetic scene.</p> <p> </p> <p><strong>When using the data in an academic context, please cite the following paper.</strong></p> <pre>@INPROCEEDINGS{10160531, author={Jawaid, Mohsi and Elms, Ethan and Latif, Yasir and Chin, Tat-Jun}, booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, title={Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing}, year={2023}, volume={}, number={}, pages={11866-11873}, keywords={Adaptation models;Satellites;Pose estimation;Lighting;Robot sensing systems;Robustness;Data models}, doi={10.1109/ICRA48891.2023.10160531} }</pre>
Computer Vision Datasets for Visual Blockage Assessment at Culverts
<p>Blockage of culverts caused by transported debris is a major factor in causing flash floods in urban areas. Traditional hydraulic models have been unsuccessful in solving this problem due to a lack of data on peak flood hydraulics and the complex behavior of debris at culverts. To address this problem, a new approach of developing intelligent video analytics (IVA) algorithms is being proposed, which uses computer vision algorithms to extract information about visual blockage. This approach is expected to help in timely and safe maintenance operations and reduce the risk of culverts being blocked. To support the development of computer vision solutions, two datasets have been created: the Synthetic Images of Culvert (SIC) and the Visual Hydraulics Lab Dataset (VHD).</p> <ul> <li>The Synthetic Images of Culvert (SIC) dataset consists of synthetic images of culverts that were generated using a 3D computer application built on the Unity3D gaming engine. The application was designed to simulate various blockage scenarios by allowing users to place different types of debris materials in the scene in various orientations and locations. These blockage scenarios were captured as images using batch capture functionality. The dataset offers diversity in terms of the type of debris (urban, vegetative, mixed), culvert types (pipe, single circular, double circular, single box, double box, triple box), camera viewpoints, time of day, and water levels. However, it has some limitations, such as a single natural background and unrealistic effects and animations.</li> <li>The Visual Hydraulics-Lab Dataset (VHD) is a dataset of simulated images of culverts that were captured during controlled hydrology lab experiments. The experiments involved a series of tests using scaled physical models of culverts under different flooding conditions. The experiments were recorded using two high definition (HD) cameras and images of culverts in both blocked and unblocked conditions were extracted. The VHD dataset includes a variety of images with different culvert configurations (single circular, double circular, single box, double box), blockage types (urban, vegetative, mixed), simulated lighting conditions, camera viewpoints, and flood levels controlled by inlet water discharge. The limitations of the dataset include reflections from the water surface and flume walls, an identical background and scaling, and clear water.</li> </ul>
ScienceDex guides
Understand access before you commit
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.