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108 results for “Thermal images”

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zenodo48/100

Hyperspectral Imaging Dataset for Laser Thermal Ablation Monitoring in Vital Organs

Objectives: The objective of the research was to use hyperspectral imaging (HSI) to detect thermal damage induced in vital organs (such as the liver, pancreas, and stomach) during laser thermal therapy. The experimental study was conducted during thermal ablation procedures on live pigs. Ethical Approval: The experiments were performed at the Institute for Image Guided Surgery in Strasbourg, France. This experimental study was approved by the local Ethical Committee on Animal Experimentation (ICOMETH No. 38.2015.01.069) and by the French Ministry of Higher Education and Research (protocol №APAFiS-19543-2019030112087889, approved on March 14, 2019). All animals were treated in accordance with the ARRIVE guidelines, the French legislation on the use and care of animals, and the guidelines of the Council of the European Union (2010/63/EU). Description: During our experimental study, we used a TIVITA hyperspectral camera to acquire hypercubes of size 640x480x100 voxels, indicating 640x480 pixels for 100 bands, and regular RGB images at each acquisition step. These bands were acquired directly from the hyperspectral camera without additional pre-processing. The hypercube was acquired in approximately 6 seconds and synchronized with the absence of breathing motion using a protocol implemented for animal anesthesia. Polyurethane markers were placed around the target area to serve as references for superimposing the hyperspectral images, which were acquired using target areas selected according to the hyperspectral camera manufacturer's guidelines. As part of our investigation, we included hyperspectral cubes from 20 experiments conducted under identical conditions in our study. The hyperspectral cubes were collected in three distinct stages. In the first stage, the cubes were gathered before laparotomy at a temperature of 37°C. In the second stage, we obtained the cubes as the temperature gradually increased from 60°C to 110°C at 10°C intervals. Finally, in the last stage, the cubes were collected after turning off the laser during the post-ablation phase. Thus, we obtained a total of 233 hyperspectral cubes, each consisting of 100 wavelengths, resulting in a dataset of 23,300 two-dimensional images. The temperature changes were recorded, and the “Temperature profile during laser ablation” image illustrates the corresponding profile, highlighting the specific time intervals during which the hyperspectral camera and laser were activated and deactivated. To provide a visual representation of the collected data, we have included several examples of images captured from different organs in the “Examples of ablation areas” figure. The raw dataset, comprising 233 hyperspectral cubes of 100 wavelengths each, was transformed into 699 single-channel images using PCA and t-SNE decompositions. These images were then divided into training and test subsets and prepared in the COCO object detection format. This COCO dataset can be used for training and testing different neural networks. Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories: <ul> <li>Source code: <a href="https://github.com/ViacheslavDanilov/hsi_analysis" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/hsi_analysis</a></li> <li>Dataset: <a href="https://doi.org/10.5281/zenodo.10444212" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10444212</a></li> <li>Models: <a href="https://doi.org/10.5281/zenodo.10444269" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10444269</a></li> </ul>

opencc-by-4.0Dec 2023View 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

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 →

zenodo44/100

ForTrunkDet - Image dataset of visible and thermal annotated images for forest tree trunk detection

Forest dataset composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by four different cameras: <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> </ul> The images and annotations are stored in two zip files: <ul> <li>forest_dataset_original.zip - original dataset</li> <li>forest_dataset_augmented - augmented dataset</li> </ul> Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.

opencc-by-4.0Aug 2021View details →

dryad40/100

Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology

Monitoring the population dynamics and behaviors of wildlife is crucial for effective conservation. Although drones can provide a promising alternative to traditional monitoring methods, validation studies must be done to quantify the accuracy of drone-based abundance and distribution estimates in various biological systems. Here, we investigate the use of drones equipped with high-resolution Red-Green-Blue (RGB) and thermal cameras, along with machine learning techniques, for assessments of abundance and physiology in northern elephant seals (Mirounga angustirostris). Aerial images of N=3,415 northern elephant seals were collected at Año Nuevo Reserve during N=24 drone flights, along with ambient air temperatures, wind speed, and time-of-day data. The two-dimensional footprints and surface temperatures of seals were measured from the images. Machine learning algorithms were applied to detect seals in the imagery, and model performance was evaluated. Our findings indicate that seal detection was more accurate using RGB images compared to Thermal images, but that Thermal images could be used to determine that time of day and ambient temperature (but not wind speed or body size) strongly influenced seal external skin temperature. In other words, RGB and Thermal cameras have different strengths and weaknesses that should be carefully considered when designing research studies. Our study highlights the promising integration of drones, thermal imaging, and machine learning for wildlife research, contributing to faster, safer, cheaper, less disruptive, and more accurate wildlife monitoring and conservation efforts.

opencc-zeroNov 2023View details →

zenodo40/100

Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top

The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The contents of the directories are as follows. "data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included. "time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.

opencc-by-4.0Nov 2021View details →

zenodo40/100

ForTrunkDetV2 - Image dataset of visible and thermal annotated images for forest tree trunk detection (augmented version)

This dataset is an augmented version of an existing dataset (<a href="https://doi.org/10.5281/zenodo.5213824">10.5281/zenodo.5213824</a>), composed by visible and thermal images with trunk annotations. The images were acquired in three different portuguese forests and were captured by five different cameras: <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> <li>OAK-D</li> </ul> The augmented images and their annotations are stored in an archive with the following structure: main_directory/ <ol> <li>annotations/ <ol> <li>pascalvoc/ <ul> <li>PascalVOC annotation files</li> <li>...</li> </ul> </li> <li>yolo/ <ul> <li>YOLO annotation files</li> <li>...</li> </ul> </li> </ol> </li> <li>images/ <ul> <li>Image files</li> <li>...</li> </ul> </li> </ol> Each annotation file links to its image by the file name, so if an image is named "img12345.jpg", its annotation files are named as "img12345.xml" (for PascalVOC format) and "img12345.txt" (for YOLO format). The dataset contains original and augmented images. The original images' names follow the pattern "img_******.jpg", where in the place of the asterisks are numbers. The remaining images are the augmented ones. Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Aerial and Terrestrial Thermal Images of German Multi-Family Buildings

This dataset consists of 968 thermal images including 693 captured by hand-held camera on the ground and 275 via UAV. Of the aerial images, 139 were recorded manually and 136 in automatic flight mode. The images depict four multi-family buildings of 18 m height in the German city of Karlsruhe belonging to the local municipal housing association Volkswohnung Karlsruhe GmbH. Table 1 gives an overview of the buildings in question, all of which were fully rented out on the days of image acquisition. Table 1: Building information <table> <tbody> <tr> <td>                      Building Details </td> <td> Sophienstr. 201-203 </td> <td> Volzstr. 2 </td> <td> Wichernstr. 4 </td> <td> Wichernstr. 10-18 </td> </tr> <tr> <td> construction year </td> <td> 1957 </td> <td> 1954 </td> <td> 1953 </td> <td> 1953 </td> </tr> <tr> <td> apartments </td> <td> 30 </td> <td> 12 </td> <td> 25 </td> <td> 30 </td> </tr> </tbody> </table> The aerial images were acquired using DJI’s “Matrice 600” UAV (DJI, 2022) equipped with the “Zenmuse XT2”, a combination of FLIR’s “Duo Pro R” thermal and RGB camera technology and DJI’s gimbal (FLIR, 2021a). All thermal images were recorded in FLIR’s proprietary image format RJPEG. The terrestrial thermographic images were captured with FLIR’s “T200” hand-held camera (FLIR, 2021b) in the standard JPEG format. The emissivity was set to 0.95 throughout the acquisition of both aerial and terrestrial images. Thermographic image processing and analysis was realized using the "FLIR Thermal Studio" software (FLIR Systems Inc., 2022). The temperature scale was set to -8 °C to +13 °C and color distribution function "signal linear" selected. The thermal images of this dataset were recorded on February 28th and March 1st, 2022, between 8 p.m. and 1 a.m. On February 28th the outside air temperature registered at between 1 °C and 3 °C. Wind speeds reached a maximum of 17 km/h. The sky was cloudless both during the flights and in the preceding 24 hours. A maximum temperature of 11 °C was recorded by local weather stations in that time period. Very similar weather conditions were present on March 1st. The outside air temperature was recorded at between -1 °C and 5 °C during acquisition, with wind speeds of max. 11 km/h. Again, the sky was entirely clear both during the flights and in the preceding 24 hours, with a maximum temperature of 9 °C present in that time period. The sun set at around 6:10 p.m. on both days (timeanddate, 2022). The images were recorded using ten different flight settings of varying speed, flight height, and camera angle. Details are summarized in Table 2. Table 2: Flight settings <table align="center"> <tbody> <tr> <td> Flight </td> <td> Building </td> <td> Automatically/ manually performed flight route </td> <td> Camera angle [°] </td> <td> Height above ground [m] </td> <td> Height above building [m] </td> <td> Distance to façade [m] </td> <td> Flight speed [m/s] </td> </tr> <tr> <td> 1 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 1 </td> </tr> <tr> <td> 2 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 3 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 5 </td> </tr> <tr> <td> 4 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 60 </td> <td> 42 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 5 </td> <td> Full area </td> <td> Automatically </td> <td> 90 (nadir) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 6 </td> <td> Full area </td> <td> Automatically </td> <td> 90 (nadir) </td> <td> 60 </td> <td> 42 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 7 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 4 </td> <td> - </td> </tr> <tr> <td> 8 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 8 </td> <td> - </td> </tr> <tr> <td> 9 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 15 </td> <td> - </td> </tr> <tr> <td> 10 </td> <td> Wichernstr. 10-18 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 15 </td> <td> - </td> </tr> </tbody> </table>   Acknowledgments: The authors appreciate the support of Marinus Vogl (Air Bavarian GmbH) in acquiring the thermal images via UAV. Moreover, they thank Harald Schneider (Karlsruhe Institute of Technology) for his advice and assistance. Lastly, they gratefully acknowledge the consent and support of Karlsruher Volkswohnung GmbH within this research project.   References: DJI (2022).  Matrice 600 - DJI. URL: https://www.dji.com/de/matrice600 (accessed 10th January 2022) FLIR (2021a).  FLIR XT2 product information (Wilsonville, USA). URL: https://www.flir.de/products/xt2/ (accessed 10th January 2022) FLIR (2021b).  FLIR T-series (Wilsonville, USA). URL: https://www.flir.com/instruments/t-series/  (accessed 10th January 2022) FLIR Systems Inc. (2022). User’s manual Flir Thermal Studio. URL: https://www.sahkonumerot.fi/6708162/doc/operatinginstructions/ (accessed 12th August 2022) Timeanddate (2022). Wetter im Februar 2022 in Karlsruhe, Baden-Württemberg, Deutschland. URL: https://www.timeanddate.de/wetter/deutschland/karlsruhe/rueckblick?month=2&year=2022  (accessed 12th March 2022)

opencc-by-4.0Oct 2022View details →

zenodo40/100

GCNT-Plume: Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning

This repository contains the relevant code and data for the paper Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning (Wei et al, 2023, Remote Sensing of Environment). Specifically, the U-Net.zip file includes the associated codes for segmenting surface thermal plumes from nuclear power plants along the global coasts and the Great Lakes by using the U-Net model integrated with prior knowledge. The GCNT-Plume.zip file includes the occurrence footprints of core area plumes (the occurrence_all folder), raw water temperature increment (WST) images (the delta folder), mixed area plumes and annotations (the extractWithLocation folder), model-predicted core area plumes (the prediction*_* folders), the mixed/core area plumes and background areas in shapefile format (the sampleAnnotation* folders), and location information (the location.xlsx table). Please refer to the README.md file in the U-Net.zip file for more detailed information.

opencc-by-4.0Jul 2023View details →

zenodo40/100

Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues

Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)

opencc-by-4.0Jul 2023View details →

dryad40/100

Evaluating the efficacy of drone-based thermal images for measuring wildlife abundance and physiology

Open the record for dataset details and reuse information.

publicDec 2023View details →

zenodo36/100

Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning

This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). This dataset containes the microstructure images (identified by particle diameters size $D_1$ and $D_2$ volume fraction $V_\mathrm{f}$ and aspect ratio $A_\mathrm{r}$) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic $\mathrm{(Hf,Ta)C/SiC}$ ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system,  respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work. Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>$D_{1}$</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>$D_{2}$</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>$V_\mathrm{f}$</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>$A_\mathrm{r}$</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> This dataset contains: <ul> <li>dataset.csv: containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li>microstructures_images.zip: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li>results.zip: contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>

opencc-by-4.0Nov 2023View details →

zenodo36/100

Imaging luminescence thermometry to 750 °C for the heat treatment of common engineering alloys and comparison with thermal imaging

Data presented in the IJOT paper: Imaging luminescence thermometry to 750 °C for the heat treatment of common engineering alloys and comparison with thermal imaging

opencc-by-4.0Nov 2021View details →

dryad36/100

Warren and entrance detections by thermal imager

<ol> <li>Thermal imaging technology is a developing field in wildlife management. Most thermal imaging work in wildlife science has been limited to larger ungulates and surface-dwelling mammals. Little work has been undertaken on the use of thermal imagers to detect fossorial animals and/or their burrows. Survey methods such as white-light spotlighting can fail to detect the presence of burrows (and therefore the animals within), particularly in areas where vegetation obscures burrows. Thermal imagers offer opportunity to detect the radiant heat from these burrows, and therefore the presence of the animal, particularly in vegetated areas. Thermal imaging technology has become increasingly available through the provision of smaller, more cost-effective units. Their integration with drone technology provides opportunities for researchers and land managers to utilise this technology in their research/management practices. </li> <li>We investigated the ability of both consumer (<AUD$20,000) and professional imagers (>AUD$65,000) mounted on drones to detect rabbit burrows (warrens) and entrances in the landscape as compared to visual assessment. </li> <li>Thermal imagery and visual inspection detected active rabbit warrens when vegetation was scarce. The presence of vegetation was a significant factor in detecting entrances (P<0.001, α=0.05). The consumer imager did not detect as many warren entrances as either the professional imager or visual inspection (P=0.009, α=0.05). Active warren entrances obscured by vegetation could not be accurately identified on exported imagery from the consumer imager and several false-positive detections occurred when reviewing this footage. </li> <li>We suggest that the exportable frame rate (Hz)was the key factor in image quality and subsequent false positive detections. This feature should be considered when selecting imagers and suggest that a minimum export rate of 30Hz is required. Thermal imagers are a useful additional tool to aid in identification of entrances for active warrens and professional imagers detected more warrens and entrances than either consumer imagers or visual inspection.</li> </ol>

opencc-zeroMar 2022View details →

zenodo36/100

RGB and Thermal Integral Image dataset for Search and Rescue with Airborne Optical Sectioning.

The `Integral Images` folder contains labels and augmented AOS integral images (both RGB and Thermal) used for training, validation and testing (`data`). The integral images are computed using the complete data that were recorded during 18 flights at 6 different sites over 10 different days.   The dataset mirrors  [YOLO (8GB)](https://zenodo.org/record/3894774/files/YOLO.zip?download=1) (`data`) for integral (`SARAOS/AOS`) images, however, now additionally contain corresponding RGB integral images in addition to corresponding thermal integral images.

opencc-by-4.0Mar 2022View details →

zenodo36/100

Real-time imaging of thermally induced microcracks in granite with ultrahigh-temperature instrument

An ultrahigh-temperature heating platform (HS1400G, Instec, Britain) on the optical microscope (BX51M, Olympus, Japan) was developed to observe the microcrack propagation of thin-section samples during heating in real time. Singapore Bukit Timah granite rock cores were examined in this study. The granite rock is widespread and one of the major rock formations in Singapore. It shows significant mechanical deterioration under the influence of heat. The rock sample was prepared as doubly polished thin sections of approximately 60 μm in thickness for petrographic analysis and microthermometric observation. Before the experiment, the slide was heated with an alcohol lamp to dissolve the resin, and the thin-slice sample was removed and immersed in acetone to wash the surface glue to avoid affecting the observation. The granite sample was heated in the following heating process: heat from room temperature of 25℃ to 100℃ at a rate of 20℃/min; maintain the temperature for 5 min, as the thin-section sample is sufficiently small to generate a homogeneous thermal field quickly; heat to 100℃, 200℃, 300℃, 400℃, 500℃, 600℃, 700℃, 800℃ and 900℃ successively; finally, reduce the temperature to room temperature at a cooling rate of 20℃/min from the maximum temperature.

opencc-by-4.0Oct 2022View details →

zenodo36/100

Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules

Numerical data and higher resolution images for Abdulkareem et al (2024), Thermal stabilisation of lysozyme through ensilication, Molecules Please cite our publication in any use of this data

opencc-by-4.0Sep 2024View details →

dryad36/100

Warren and entrance detections by thermal imager

Open the record for dataset details and reuse information.

publicMar 2022View details →

dryad36/100

Data from: Thermal imaging demonstrates physiological responses to grooming interactions and audience effects in wild baboons

Open the record for dataset details and reuse information.

publicOct 2025View details →

zenodo32/100

UAV thermal image dataset

The thermal images dataset is captured by DJI Matrice 210 Drone equipped with FLIR thermal camera for SAR operation.

opencc-by-4.0Dec 2020View details →

ScienceDex guides

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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