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zenodo48/100

SCLabels: Labelled rectified RGB images from the Spanish CoastSnap network

<h1>Training dataset</h1> The SCLabels dataset is intended to be used in the exploring and development of Artificial Intelligence (AI) applications aimed at the automation of the shoreline extraction process from rectified images. SCLabels includes rectified RGB images from the Spanish CoastSnap network and their corresponding masks, together with a metadata file and a README file. RGB images encompass variable geographic locations, fields of view, beach types and degrees of occupation, tidal regimes, meteoceanic and lightning conditions, and a variety of environmental characteristics. Masks account for dense pixel labels including 5 categories: i) No data; ii) Not classified; iii) Landwards; iv) Seawards; and v) Shoreline. In the metadata file, images are linked to their corresponding masks, and information about the geographic location of each image, capture characteristics and image source, shoreline position and other auxiliary data are provided. The README file enhances the explainability and comprehension of the dataset, elaborating on the context and contents, and providing detailed explanations of the metadata, potential limitations, technical aspects of the image processing and annotation stages, usage recommendations, and related works.   <h1>Technical details</h1> The SCLabels dataset version 1.0.0 is packaged in a compressed file (SCLabels_v1.0.0.zip). A total of 1717 RGB images are shared in JPG format, corresponding masks in PNG format, a metadata file in JSON format, and the README file in PDF format. <h2>Data preprocessing</h2> To generate the SCLabels masks, rectified RGB images and their corresponding shorelines were used. RGB images were cropped to the minimum and maximum alongshore pixel coordinates of the shoreline (vertical axis) plus 10 additional pixels above and below to preserve contextual information. A grayscale image was then derived from each cropped RGB image for subsequent pixel labelling. First, a binary mask was derived, marking "NoData'' for black and white padded pixels resulting from the registration and rectification steps. Subsequently, the shoreline was densified, ensuring at least one pixel per row was assigned the "Shoreline" label. Next, "Landwards" and "Seawards" labels were assigned to the right and left of the shoreline. Pixels left unlabelled were categorised as "NotClassified". Finally, masks’ values were reclassified to align with the predefined labels, and the grayscale masks were exported. For additional information, please consult the README file.  <h2>Data splitting</h2> Data splitting requirements may vary depending on the chosen AI approach (e.g., splitting by entire images, image patches, or image rows). Researchers should use a consistent data splitting method and document the approach and splits used in publications. This transparency enables reproducible results and facilitates comparisons between studies. <h2>Classes, labels and annotations</h2> The SCLabels dataset includes one mask per rectified RGB image, sharing the same width and height. These masks are in greyscale and PNG format, and consist of five different labels: <table> <tbody> <tr> <td> Mask value</td> <td>        Label</td> <td>                                                                             Description</td> </tr> <tr> <td>0</td> <td>NoData</td> <td>High probability of being black or white padded pixels, used to pad non-rectangular images within the image registration and rectification processes</td> </tr> <tr> <td>25</td> <td>NotClassified</td> <td>Not labeled pixels</td> </tr> <tr> <td>75</td> <td>Landwards</td> <td>All pixels that are towards the landside with respect to the shoreline (row-wise), excluding “NoData” ones</td> </tr> <tr> <td>150</td> <td>Seawards</td> <td>All pixels that are towards the seaside with respect to the shoreline (row-wise), excluding “NoData” ones</td> </tr> <tr> <td>255</td> <td>Shoreline</td> <td>Pixels intersected by the mapped shoreline densified to cover one pixel per row, at least</td> </tr> </tbody> </table> <h2>Parameters</h2> RGB values or any transformation in the colour space can be used as parameters. <h2>Data sources</h2> In the  CoastSnap initiative, citizens capture images (oblique smartphone photos) from fixed CoastSnap stations and share them with the scientific managers. Images are subjected to a quality control process, spatially registered to a designated target image, and rectified (georeferencing). The shoreline is subsequently digitised from each rectified image. <h2>Data quality</h2> All images included have been supervised by CSs’ scientific managers. However, citizen scientists take images by smartphones (different camera quality) at irregular intervals across various sites with varying weather and illumination conditions. Users of SCLabels dataset must be aware of this variance. <h2>Image resolution</h2> The resolution of the images depends on the CoastSnap station and the length of the shoreline, ranging from 241x188 pixels to 801x796 pixels. <h2>Spatial coverage</h2> The SCLabels dataset version 1.0.0 contains data from five Spanish CoastSnap stations, including sandy beaches in the northwest (agrelo), the Cíes Islands (cies), the south (cadiz), and the Balearic Islands (samarador and arenaldentem). <table> <tbody> <tr> <td>  CoastSnap station</td> <td> Longitude</td> <td>  Latitude</td> </tr> <tr> <td>agrelo</td> <td>-8.772</td> <td>42.331</td> </tr> <tr> <td>cies</td> <td>-8.900</td> <td>42.226</td> </tr> <tr> <td>cadiz</td> <td>-6.288</td> <td>36.522</td> </tr> <tr> <td>samarador</td> <td>3.185</td> <td>39.350</td> </tr> <tr> <td>arenaldentem</td> <td>2.974</td> <td>39.353</td> </tr> </tbody> </table> <h2>Contact information</h2> For further technical inquiries or additional information about the annotated dataset, please contact jsoriano@socib.es.

opencc-by-4.0Nov 2023View details →

zenodo48/100

Sentinel2GlobalLULC: A dataset of Sentinel-2 georeferenced RGB imagery annotated for global land use/land cover mapping with deep learning (License CC BY 4.0)

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE).    Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames):  <ul> <li>Land Cover Class ID: is the identification number of each LULC class</li> <li>Land Cover Class Short Name: is the short name of each LULC class</li> <li>Image ID: is the identification number of each image within its corresponding LULC class </li> <li>Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products </li> <li>GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image</li> <li>Latitude: is the latitude of the center point of each image</li> <li>Longitude: is the longitude of the center point of each image</li> <li>Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes</li> <li>Administrative Department Level1: is the administrative level 1 name to which each image belongs</li> <li>Administrative Department Level2: is the administrative level 2 name to which each image belongs</li> <li>Locality: is the name of the locality to which each image belongs</li> <li>Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile</li> </ul> For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files: <ul> <li>A CSV file that contains all exported images for this class </li> <li>A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images".</li> </ul> To clearly state the geographical coverage of images available in this dataset,  we included in the version v2.1,  a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name. © <a href="https://doi.org/10.5281/zenodo.5055632">Sentinel2GlobalLULC Dataset </a>by  Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)

opencc-by-4.0Jul 2022View details →

zenodo48/100

Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations

Overview: The dataset of Thermal Bridges on Building Rooftops (TBBR dataset) consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to 2680x3370 to remove empty borders. See the "Usage" section below for details about the stored formats made available here. The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. The dataset contains 926 images with a total of 6,927 annotations of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles. For the annotation of the thermal images the image processing program VGG Image Annotator from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated. Usage: Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height]. Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g. <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> these will be decompressed into the file structure: <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below. Note: The annotation files contain relative paths to numpy files, in case of problems please convert to absolute paths (i.e. insert the containing directory before each file path in the JSON annotation files). We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries. We recommend the following folder structure for use: <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> Metadata: The experimental metadata was structured with the Spatio Temporal Asset Catalog (STAC) specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis. One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded. This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata. Metadata of the archived NumPy files for each image was structured using the Data Package schema from the Frictionless Standards. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file. Note that camera1 corresponds to the RGB camera and camera2 the thermal. FAIR Digital Objects: All files are represented in a standardized way as FAIR Digital Objects (FAIR DOs) to enable machine actionable decisions on the data in spirit of the FAIR principles. Persistent Identifier (PID): Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>. <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>

opencc-by-4.0Aug 2022View details →

zenodo48/100

ZHAW-ISC 3D ToF and RGB Fusion Dataset

This dataset contains depth maps recorded by an ESPROS epc635 Time-of-Flight camera and RGB images from a Raspberry Pi camera module V2, for use in the fusion of these sensors to increase the resolution of the ToF camera. The dataset contains three scenes, one of a paper dodecahedron, a wooden grid with holes of various sizes, and a set of wooden bars with different distances between them. All scenes have a black background with low reflectivity at the 3D ToF camera’s illumination wavelength to reduce multi-path interference. An HDR image is created by combining two 3D ToF depth maps with different integration times based on the recorded amplitude of each pixel. The depth map is then transformed to the perspective of the RGB camera and the resulting holes are filled with the mean of their neighboring pixels. The content of the included files are: <ul> <li>Recorded HDR ToF camera depth maps (160x60), containing the depth values in millimeters, stored as 16-bit PNG files. </li> <li>Recorded RGB images (2560x960)</li> </ul>

opencc-by-4.0Oct 2024View details →

zenodo48/100

Data for estimating spruce tree health using drone-based RGB and multispectral imagery

The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a>  The image data was acquired between 11th and 14th September 2020. RE = Red-Edge M multispectral data RGB = RGB data (Phantom 4 Pro) Altum = Altum multispectral data The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively. The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above. Key for Field Reference: Z = tree height dbh = diameter-at-breast-height (cm) vari = Discoloration (score 0-5) harsu = Defoliation (score 0-4) pihka = Resin flows (score 0-2) runko = Stem/bark structural damage (score 0-2) latvus = Significantly decreased canopy size (score 0-1)

opencc-by-4.0Oct 2024View details →

zenodo48/100

RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022

The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.

opencc-by-4.0Oct 2024View details →

zenodo48/100

Dataset on UAV RGB videos acquired over a vineyard property of Bodegas Terras Gauda at an early stage of Botrytis cinerea infection in 2021

The videos were collected in a vineyard owned by Bodegas Terras Gauda, in June 2021. The videos were collected with a DJI Matrice 210 RTK UAV, which had a DJI Zenmuse X5S sensor onboard. A total of 4 rows were recorded with side videos. The flights were carried out on a sunny day with wind velocity lower than 0.5 m/s. Annotations of the grape clusters in the MOTS style are provided.

opencc-by-4.0Dec 2020View details →

zenodo48/100

Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

Overview: This repository contains the raw images for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. This dataset contains 5696 drone images (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. Usage: Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal: <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> File Numbering/Naming Scheme: The pairs of RGB + Thermal images follow the simple numbering scheme of: RGB = Thermal + 1. For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image. To perform the merging, we recommend using the merge_image_layers.py script provided by the associated <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a> (see the scripts/alignment/ directory). For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. The CSV format is as follows: <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre>

opencc-by-4.0Nov 2022View details →

zenodo48/100

Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332   The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: <ol> <li>water</li> <li>bare sediment</li> <li>vegetation</li> <li>development (roads, buildings, power lines, parking lots, etc.)</li> </ol>   Included here are 6 files with the same root name: <ol> <li> '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction.</li> <li>'.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`.</li> <li> '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</li> <li> '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</li> <li>'.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py`</li> <li>'_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</li> </ol> Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

opencc-by-4.0Feb 2023View details →

zenodo48/100

Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments

ContextThis dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads, DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image. ContentThis repository contains three .zip folders each containing data collected in a different lab or field trial.<ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a> AcknowledgementsThe data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).

opencc-by-4.0Mar 2023View details →

edi48/100

Uncalibrated RGB orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.

Uncalibrated RGB data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.

openCC (other)Apr 2022View details →

zenodo44/100

IODP Expedition 391 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Oct 2023View details →

zenodo44/100

IODP Expedition 397T RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Oct 2023View details →

zenodo44/100

IODP Expedition 383 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Jul 2021View 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 →

zenodo44/100

Ultra-high-resolution modified RGB UAV-imaging of Alternaria solani

This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation).  This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information: <ul> <li>Original patch name</li> <li>Random patch name (used during the labeling process)</li> <li>Row patch number</li> <li>Column patch number</li> <li>Block number, column block number and row block number</li> <li>Original mage name</li> <li>Coordinates of original image: latitude, longitude, altitude</li> <li>Date of flight</li> <li>Label (0: no Alternaria, 1: Alternaria)</li> </ul> More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.'    If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998"   This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).

opencc-by-4.0Feb 2024View details →

zenodo44/100

IODP Expedition 378 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Feb 2022View details →

zenodo44/100

IODP Expedition 367 RGB channels (calculated from core photos)

Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.

opencc-by-4.0Sep 2018View details →

zenodo44/100

Kelvin (color temperature) to RGB conversion table.

This CSV provides the corresponding RGB values for a specific color temperature (measured in Kelvin). It can be used to determine the appropriate RGB color values for a given color temperature. This dataset provides an approximation following the <a href="https://cie.co.at/datatable/cie-1964-colour-matching-functions-10-degree-observer">CIE 1964 colour-matching functions</a> that is intended for low to mid quality output media (such as LED lighting, consumer screens and consumer grade VR headsets).

opencc-by-4.0Nov 2024View details →

zenodo44/100

RGB pixels VALUES FOR APPLES/LETTUCE AI OPTICAL RECOGNITION - 5 categories of Freshness

The Datasets include RGB color pallete per  pixel values for optical recognition on apples/lettuce and freshness categorized using AI Algorithm . Those Datasets are for AI Training projects . It will be used on the stage of creation, verification or optimization for new optical AI models. The tables can be used direclty on the AI tools, inserted and using the pixels colors number for every category. The freshness categories are 5, from the highest- crop day (5) to the lowest - not for eating (1).

opencc-by-4.0Nov 2024View details →

ScienceDex guides

Understand access before you commit

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

Compare curated 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.

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