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zenodo44/100

Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

This dataset contains the video clips used to produce the results presented in:Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

opencc-by-4.0Nov 2023View details →

zenodo44/100

Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation

<h1>Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation</h1> <h2>Introduction</h2> This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project <a href="https://robotik.dfki-bremen.de/en/research/projects/deepersense/">DeeperSense</a> (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. <a href="../records/7728089">Parts</a> <a href="../records/10220989">of</a> the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the <a href="https://www.go-fair.org/fair-principles/">FAIR</a> principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately. <h3>Locations and sensors</h3> The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The <code>examples/</code> directory holds a typical output image for each sensor at each available location. <h3>Data volume per session</h3> Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session: <table> <tbody> <tr> <th>Session dates</th> <th>Location</th> <th>Number of datasets</th> <th>Total duration of datasets [h]</th> <th>Total logfile size [GB]</th> <th>Number of images</th> <th>Total image size [GB]</th> </tr> <tr> <td>2021-08-09 - 2021-08-12</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>52</td> <td>10.8</td> <td>28.8</td> <td>389’047</td> <td>88.1</td> </tr> <tr> <td>2022-02-07 - 2022-02-08</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>35</td> <td>4.4</td> <td>54.1</td> <td>629’626</td> <td>62.3</td> </tr> <tr> <td>2022-04-26 - 2022-04-28</td> <td>Chalk Lake Hemmoor</td> <td>52</td> <td>8.1</td> <td>133.6</td> <td>1’114’281</td> <td>97.8</td> </tr> <tr> <td>2022-06-28 - 2022-06-29</td> <td>Tank Wash Basin Neu-Ulm</td> <td>42</td> <td>6.7</td> <td>144.2</td> <td>824’969</td> <td>26.9</td> </tr> <tr> <td>2023-04-26 - 2023-04-27</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>55</td> <td>7.4</td> <td>141.9</td> <td>739’613</td> <td>9.6</td> </tr> <tr> <td>2023-09-01 - 2023-09-02</td> <td>Lake Starnberg</td> <td>19</td> <td>2.9</td> <td>40.1</td> <td>217’385</td> <td>2.3</td> </tr> <tr> <th> </th> <th> </th> <th>255</th> <th>40.3</th> <th>542.7</th> <th>3’914’921</th> <th>287.0</th> </tr> </tbody> </table> <h2>Data and metadata structure</h2> <h3>Sensor data corpus</h3> The sensor data corpus comprises two processing stages: <ul> <li>raw data streams stored in ROS bagfiles (aka logfiles),</li> <li>camera and sonar images (aka datafiles) extracted from the logfiles.</li> </ul> The files are stored in a file tree hierarchy which groups them by session, dataset, and modality: <pre><code>${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}</code></pre> A typical logfile path has this form: <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag</code></pre> A typical datafile path has this form: <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg</code></pre> All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file <code>entities.json</code>. <h3>Metadatabase</h3> The metadatabase is provided in two equivalent forms: <ul> <li>as a standalone <a href="https://www.sqlite.org/index.html">SQLite</a> (https://www.sqlite.org/index.html) database file <code>metadata.sqlite</code> for users familiar with SQLite,</li> <li>as a collection of CSV files in the <code>csv/</code> directory for users who prefer other tools.</li> </ul> The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information. An entity relationship diagram of the metadatabase tables is stored in the file <code>entity_relationship_diagram.png</code>. Each entity, its attributes, and relations are documented in detail in the file <code>entities.json</code> Some general design remarks: <ul> <li>For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.</li> <li>In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities <code>stream</code> and <code>logfile</code> are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.</li> <li>A <code>modality</code> is not an attribute of a <code>sensor</code> alone, but of a <code>datafile</code>: Because a <code>sensor</code> is an attribute of a <code>stream</code>, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.</li> </ul> As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query: <div> <pre><code>SELECT PRINTF( '%s - %s', SUBSTR(session_start, 1, 10), SUBSTR(session_end, 1, 10)) AS 'Session dates', location_name_english AS Location, number_of_datasets AS 'Number of datasets', total_duration_of_datasets_h AS 'Total duration of datasets [h]', total_logfile_size_gb AS 'Total logfile size [GB]', number_of_images AS 'Number of images', total_image_size_gb AS 'Total image size [GB]' FROM location JOIN session USING (location_id) JOIN ( SELECT session_id, COUNT(dataset_id) AS number_of_datasets, ROUND( SUM(dataset_duration) / 3600, 1) AS total_duration_of_datasets_h, ROUND( SUM(total_logfile_size) / 10e9, 1) AS total_logfile_size_gb FROM location JOIN session USING (location_id) JOIN dataset USING (session_id) JOIN view__dataset_total_logfile_size USING (dataset_id) GROUP BY session_id ) USING (session_id) JOIN ( SELECT session_id, COUNT(datafile_id) AS number_of_images, ROUND(SUM(datafile_size) / 10e9, 1) AS total_image_size_gb FROM session JOIN dataset USING (session_id) JOIN stream USING (dataset_id) JOIN datafile USING (stream_id) GROUP BY session_id ) USING (session_id) ORDER BY session_id;</code></pre> </div>

opencc-by-4.0Dec 2023View details →

zenodo40/100

SC-XRD diffraction images of stereo-defined piperazine (2/2)

Structures of a piperazine in the publication: Suarez-Pantiga, S.; Colas, K.; Johansson, M. J.; Mendoza, A.* "Scalable synthesis of piperazines enabled by visible light irradiation and aluminum organometallics" Angew. Chem. Int. Ed. 2015, 54, 14094–14098 Structure solutions were deposited in the CCDC: 1052437 (3b - PiPy3Me) https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201505608

opencc-by-4.0Jan 2017View details →

zenodo40/100

SC-XRD diffraction images of stereo-defined piperazines (1/2)

Structures of two piperazines in the publication: Suarez-Pantiga, S.; Colas, K.; Johansson, M. J.; Mendoza, A.* "Scalable synthesis of piperazines enabled by visible light irradiation and aluminum organometallics" Angew. Chem. Int. Ed. 2015, 54, 14094–14098 Structure solutions were deposited in the CCDC: 1052438 (3g - PiPy5Br) and 1053734 (3ij - PiPyzIm) https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201505608

opencc-by-4.0Jan 2017View details →

zenodo40/100

Digital Elevation Model from Pléiades stereo images - Upper Tuolumne basin, California, 2017-08-13

Digital Elevation Model (DEM) derived from a triplet of stereo images of the Pléiades satellite. The images were acquired on 13th August 2017 and cover a part of the upper Tuolumne basin (California). The DEM was calculated at a 3 m resolution with the Ames Stereo Pipeline software (Beyer et al., 2018). Details about the processing of the images and the accuracy of the DEM are given in Deschamps-Berger et al. (2020).   References Beyer, R. A., Alexandrov, O., and McMichael, S. (2018). The Ames Stereo Pipeline: NASA’s open source software for deriving and processing terrain data. Earth and Space Science, 5, 537–548. https://doi.org/10.1029/2018EA00040 Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M. (2020). Snow depth mapping from stereo satellite imagery in mountainous terrain : evaluation using airborne lidar data. The Cryosphere, (February):1–28. https://doi.org/10.5194/tc-14-2925-2020

opencc-by-4.0Apr 2022View details →

zenodo40/100

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit)

opencc-by-4.0Aug 2022View details →

zenodo40/100

Pleiades snow dataset from stereo-images

Snow depth satellite observation datasets made from Pleiades stereo-images, used in the evaluation of the SURFEX/Crocus/SnowPappus simulation system.Datasets are in the NETCDF4_CLASSIC format.The observed snow depth field is named 'DSN_T_ISBA' with a 250m horizontal resolution.Datasets projections are 'EPSG:2154' and is described in the Projection_Type field.Datasets are CF Compliant.

opencc-by-4.0Oct 2023View details →

zenodo36/100

Text-fig. 18. Stereo images of fossil marine gastropod steinkerns from White Patch Fossil Site 1. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique

Text-fig. 18. Stereo images of fossil marine gastropod steinkerns from White Patch Fossil Site 1.

opencc-by-4.0Dec 2021View details →

zenodo36/100

SC-XRD diffraction images of stereo-defined pyrrolidine ligand

Structures of a piperazine in the publication: "Direct and Stereospecific [3+2] Synthesis of Pyrrolidines from Simple Unactivated Alkenes" Angew. Chem. Int. Ed. 2017, DOI:10.1002/anie.201706682 Structure solutions were deposited in the CCDC: 1528080 https://www.ccdc.cam.ac.uk/structures-beta/Search?id=doi:10.1002/anie.201706682

opencc-by-4.0Aug 2017View details →

zenodo32/100

The Light Field & Stereo (LFS) Image Dataset

This dataset contains a collection of monocular light field raw images, stereo-paired images, and calibration files for both the light field camera and the stereo camera. It also includes rectified stereo and depth images as computed using the SGM stereo method, and these are further reprojected to the extrinsically and intrinsically calibrated light field camera frame. This dataset is intended to aid research in computational photography, computer vision, image processing, and related fields. The varied, static images have been captured indoors, aiming to minimize the size of the dataset while maximizing its variability.

opencc-by-4.0Nov 2024View details →

zenodo32/100

Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138

The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). Journal of South American Earth Sciences ). The dataset provided should be cited together with the article.  DSM processing  The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID:  SEN_SPOT7_20180419_142955500_000, delivered by CONAE as DS_SPOT7_20180419). The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps:  <ol> <li> The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable. </li> <li> The scenes were map-projected onto the NASADEM (spatial resolution of 30 m)  elevation dataset, assisted by the results of the orbital adjustment in Step 1. </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way:  Correlation Kernel: 15 x 15 pixels; Sub-pixel Refinement Kernel: 21 x 21 pixels; Subpixel Refinement Mode: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as DSM_Copahue_UTM19S_WGS84_5m_raw.tif). </li> </ol> The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEMTM  elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m.  Comprehensive details on the methodologies evaluated  to create the dataset with ASP, can be found in the corresponding master's thesis  “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>).  Creation of the Final, Filtered DSM product The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps.    <ol> <li> Water Bodies Delineation </li> </ol> The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas  above this threshold were filled in with a constant value and their borders  were smoothed with a median filter to provide smooth shorelines. 2 . Void Filling Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software.   3. Smoothing Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel  window, excluding water bodies filled in the step 1.   Final Remarks and Suggestion The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.   Dataset Description <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> No Data Value = -9999 Format = float 32 bit File Format = GeoTiff Vertical Datum: WGS84 Projection information: EPSG 32719 (UTM19S) Spatial Resolution: 5m (subfix: _5m)  Versions:  <ul> <li> Unfiltered product: without corrections DSM_Copahue_UTM19S_WGS84_5m_raw.tif </li> <li> Final, filtered product: smoothed and void filled DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> No Lake Value = 0 Lakes Values = 1 to 45 File Format= GeoTiff Spatial Resolution: 5m (subfix: _5m) Projection information : EPSG 32719 (UTM19S) WB_mask_5m_UTM19S.tif </td> </tr> </tbody> </table>     Repository structure |__ 01_Scripts     |+ run21_CopahueDSM_AMES_sviotto.sh     |+ stereo.default |__ 02_DSMs     |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif     |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif     |+  WB_mask_5m_UTM19S.tif References Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. Earth and Space Science, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a> Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015. Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. Journal of Geophysical Research: Earth Surface, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a> Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138.  https://doi.org/10.1016/j.jsames.2022.104138

opencc-by-4.0Dec 2022View details →

zenodo32/100

PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images

We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.

opencc-by-4.0Jul 2023View details →

ClinicalTrials.gov32/100

Stereo Photogrammetry Imaging in Normal Volunteers and Patients With Head and Facial Malformations

ClinicalTrials.gov study NCT00100529. IPD Sharing: Not stated. Countries: 3. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

zenodo28/100

SQUID- Stereo Quantitative Underwater Image Dataset

Underwater Single Image Color Restoration SQUID- Stereo Quantitative Underwater Image Dataset <a href="http://www.eng.tau.ac.il/~berman">Dana Berman</a>, Deborah Levy, <a href="http://www.eng.tau.ac.il/~avidan/">Shai Avidan, </a><a href="https://www.viseaon.haifa.ac.il/">Tali Treibitz</a>   Abstract Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.   Publication The paper is availbale on <a href="https://arxiv.org/abs/1811.01343">arXiv</a>. If you use this dataset please cite it as SQUID [ref]. [Ref] Berman, Dana, Deborah Levy, Shai Avidan, and Tali Treibitz. "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020). Bibtex entry: @article{berman2020underwater, title={Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset}, author={Berman, Dana and Levy, Deborah and Avidan, Shai and Treibitz, Tali}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2020} } Source Code The code is availbale on GitHub: <a href="https://github.com/danaberman/underwater-hl">https://github.com/danaberman/underwater-hl</a>. Dataset The dataset includes RAW images, TIF files, camera clibration files, and distance maps. The database contains 57 stereo pairs from four different sites in Israel, two in the Red Sea (representing tropical water) and two in the Mediterranean Sea (temperate water). In the Red Sea the sites were a coral reef ('Katzaa') which is 10-15 meters deep (15 pairs) and a shipwreck ('Satil'), 20-30 meters deep (8 pairs). In the Mediterranean Sea both sites were rocky reef environments, separated by 30km, Nachsholim at 3-6 meters depth (13 pairs), and Mikhmoret at 10-12 meters depth (21 pairs). For convenience it is divided to the 4 dive sites. <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/README.md">README file</a>. If you use this data, please <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/underwater_haze_lines_and_dataset.bib">cite the paper</a>. To evaluate your own results, please use this <a href="http://csms.haifa.ac.il/profiles/tTreibitz/datasets/ambient_forwardlooking/files/underwater-dataset-evaluation-undisorted-params.zip">evaluation code</a>.   References [Drews et al. 2013] P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos. Transmission estimation in underwater single images. In Proc. IEEE ICCV Underwater Vision Workshop, pages 825–830, 2013. [Peng et al. 2015] Y.-T. Peng, X. Zhao, and P. C. Cosman. Single underwater image enhancement using depth estimation based on blurriness. In Proc. IEEE ICIP, 2015. [Ancuti et al. 2016] C. Ancuti, C. O. Ancuti, C. De Vleeschouwer, R. Garcia, and A. C. Bovik. Multi-scale underwater descattering. In Proc. ICPR, 2016. [Ancuti et al. 2017] C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, L. Neumann, and R. Garcia. Color transfer for underwater dehazing and depth estimation. In Proc. IEEE ICIP, 2017(All color transfers were done with a single image). [Emberton et al. 2017]S. Emberton, L. Chittka, and A. Cavallaro, Underwater image and video dehazing with pure haze region segmentation, Computer Vision and Image Understanding, 2017. [Ancuti et al. 2018] . O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert. Color balance and fusion for underwater image enhancement. IEEE Transactions on Image Processing, 27(1):379–393, 2018.

opencc-by-nc-sa-4.0Nov 2021View details →

zenodo28/100

Map of the rate of elevation change of Mont-Blanc glaciers derived from Pléiades stereo-images

This collection contains three map of elevation changes derived form Pléiades images acquired in 2012, 2021 and 2022 over the Mont-Blanc Massif (Alps) with a horizontal grid spacing of 4 m. They were derived from stereo-pairs acquired 19 August 2012, 15 August 2021, 12 October 2022, 8 August 2022 and 5 October 2022. No data are indicated by the -9999 value. We generated 4-m DEMs from five Pléiades stereo-pairs using the semi-global matching algorithm of the Ames Stereo Pipeline (Beyer et al., 2018) and the set of parameters defined in Deschamps-Berger et al. (2020). The DEMs were first coregistered on stable terrain, masking out glacierized areas using a glacier inventory from year 2015 (Paul et al., 2020). Spatially-coherent biases in the elevation difference maps were next corrected using a polynomial fit across-track and a spline fit in the along-track direction (Falaschi et al., 2023).  Pléiades stereo-images were obtained thanks to DINAMIS Project, for “Dispositif Institutionnel National d’Approvisionnement Mutualisé en Imagerie Satellitaire”, a French platform that acquires and distributes very high resolution Earth satellite imagery for French and foreign institutional users under specific subscription conditions.

opencc-by-4.0Jun 2023View details →

nasa28/100

NAMMA TWO-DIMENSIONAL STEREO PROBE AND CLOUD PARTICLE IMAGER V1

The NAMMA Two-Dimensional Stereo Probe and Cloud Particle Imager dataset consists of data from two probes used to measure the size, shape, and concentration of cloud particles; the two-dimensional stereo probe (2D-S), and the cloud particle imager (CPI). Both of these probes measure particle size distributions and derives extinction, particle concentration, ice water content and particle shape. Both probes provide hi-resolution black and white images of cloud particles. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.

restrictednotspecifiedApr 2025View details →

nasa28/100

CMS: Mangrove Canopy Height from High-resolution Stereo Image Pairs, Mozambique, 2012

This data set provides canopy height estimates for mangrove forests at 0.6 x 0.6 m resolution in three study sites located in southeastern Mozambique, Africa: two sites on Inhaca Island and one in the Maputo Elephant Reserve, located in the southern province of Maputo for September, 2012. The estimates were derived from WorldView1 (WV-1) very high resolution (VHR) stereo images processed using the Ames Stereo Pipeline (ASP) digital surface model (DSM) tool.

restrictednotspecifiedApr 2025View details →

ClinicalTrials.gov24/100

Photometric Stereo Endoscopy (PSE) as a Tool for Imaging the Rectum and Colon

ClinicalTrials.gov study NCT01979458. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov20/100

A New Reproducible Method of Measuring C/D Ratio of Digital Stereo Images of Optic Disc

ClinicalTrials.gov study NCT01683942. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

nasa20/100

PHOENIX MARS SURFACE STEREO IMAGER 5 INCID OVER FLX SCI V1.0

The Surface Stereo Imager (SSI) experiment on the Mars Phoenix Lander consists of one instrument component plus command electronics. This SSI Imaging Science RDR data set contains Incidence Over Flux (IOF) data from the Surface Stereo Imager (SSI).

restrictedus-pdMar 2025View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record