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401 results for “UAVs”

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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

UAV-derived DEM and DOM along the co-seismic surface ruptures produced by the Mw5.7 aftershock during the 22-01-2024, Mw7.0 Wushi earthquake, Xinjiang, China

This unmanned aerial vehicle (UAV) dataset was acquired by a DJI Matrice 300 RTK and a DJI Phantom 4 Pro, on February 3 and 5, 2024, respectively. The Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM) were processed using the Agisoft Metashape Professional software. These data were used to map the co-seismic surface ruptures produced by the 29-01-2024, Mw5.7 aftershock following the 22-01-2024 Mw7.0 Wushi mainshock, Xinjiang, China, and to measure the associated vertical offsets along the surface ruptures.

opencc-by-4.0Apr 2024View details →

zenodo44/100

Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'

This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637). In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.

opencc-by-4.0Dec 2024View details →

zenodo44/100

Collaborative UAV-based Orthomosaic of Isimila, Tanzania

An UAV-based orthomosaic of the Middle-Pleistocene archaeological site of Isimila, Tanzania. This version is for the expressed purpose of fostering collaboration of research at the site. GPS coordinates of excavation trenches, surface finds, and other points of interest submitted by any researchers working at the site will be plotted on this regularly updated map.  Instructions for submission and contact are available here: https://docs.google.com/document/d/12O3TN7-NuqaszEsiHJ7YUBSztQb1hPfOmaFDy8Zw8BU/edit?usp=sharing

opencc-by-4.0Dec 2018View details →

zenodo44/100

FlexiGroBots - Blueberry orchard UAV dataset

FlexiGroBots - Blueberry orchard UAV dataset Acquisition date: 02.07.2021. Location: Babe, Serbia Dataset consists of UAV drone images: - 6 channels: reflectance blue, reflectance green, reflectance red, reflectance red edge, reflectance NIR, RGB. In order to align different channels, registration was applied and because of uneven illumination during the acquisition process, illumination correction was performed. Thus, the results of each preprocessing step are located in separate folders, i.e. raw data are in 100FPLAN and 101FPLAN, results of registration are in 100FPLAN_registrated and 101FPLAN_registrated, while images with corrected illumination are in 100FPLAN_registrated_corrected and 101FPLAN_registrated_corrected. Orthomosaics created before and after preprocessing are located in UAV orthomosaics folder.

opencc-by-4.0Nov 2021View details →

zenodo44/100

FlexiGroBots - Blueberry UAV Hyperspectral Dataset

Acquisition dates: 07.07.2021; 16.07.2021; 12.09.2021 Location: Mai&scaron;iagala, Vilnius District Municipality, Lithuania Spatial resolution: 0.023 m/pixel Number of spectral bands: 204 Spectral range: 426-958 nm (visible-near infrared spectrum) Spectral resolution: 2.8 nm Flight altitude: 70 m   The dataset consists of blueberry hyperspectral imaging data acquired with a UAV and a BaySpec OCI-F Hyperspectral Imager on several dates. In total, six flights on three different dates were performed. The data from each UAV flight are given as a separate dataset. Each dataset consists of raw and processed hyperspectral imaging data. The raw data include calibration images of white reference and dark background, raw hyperspectral images, and information on the UAV flight path. Calibration data are stored in the folders "...-White", "...-White_FS2", "...-Dark", and "...-Dark_FS2". Raw images are located in subfolders RawImages and RawImages_FS2 of the main data folder, which ends with "..._BI08". The BaySpec Cube Creator 2100 software was used to process raw images into hyperspectral data cubes, which are provided in the format of band sequential image files (BSQ). BSQ files are located in the Cube folders of each dataset together with HDR files containing metadata for each cube. The values of hyperspectral data cubes are in digital numbers, which can be recalculated to reflectance using the reflectance scaling factor. It is specified in the HDR files for each cube individually. Datasheet of the dataset: <a href="https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W">https://drive.google.com/file/d/1QV5he5bGazAlN8A5Mpyl1xMc7lQcvy9W</a> Download links: <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-07.zip (60.48 GB)</a> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-07-16.zip (177.22 GB)</a> <a href="https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip">https://blueberry-dataset.s3.eu-west-1.amazonaws.com/2021-09-15.zip (81.12 GB)</a>

opencc-by-4.0Apr 2022View details →

zenodo44/100

UAV Laser Scanning surveys of the lake terminating glacier Fjallsjokull in SE Iceland, captured in July, 2021.

This dataset consists of 5 separate laser scanning surveys performed between the 8th and 15th July, 2021. Two surveys were conducted in the morning and afternoon of the 8th and the 9th, and then only the morning of the 15th. The point clouds have been cleaned to remove erroneous points. The point clouds were processed using the methods and code available at <a href="https://github.com/christomsett/Direct_Georeferencing">Direct_Georeferencing</a>. All point clouds are georeferenced in the projected WGS 1984 UTM 28N system, and provided in the widely used compressed 'laz' format. An accuracy assessment of the data showed that all surveys were consistent to within 0.1 m of each other, apart from the second flight (afternoon) on the 8th July. Any users of this data should be aware of its limitations in a challenging cryospheric environment.

opencc-by-4.0Aug 2022View details →

zenodo44/100

Processed UAV orthomosaics and DEMs of Fjallsjökull, southeast Iceland (2 of 2)

Orthomosaics of the lower ice surface of Fjallsjökull, southeast Iceland, produced from high resolution UAV surveys undertaken in July 2021. This data was produced as part of my PhD research, and therefore, forms a significant component of my final PhD thesis. All files are in UTM Zone 28N. The resolution of the all the uploaded orthomosaics in this dataset is 0.02 m.  Please note this is dataset 2 out of 2 (due to the 50 GB limit of file uploads). The DEMs and orthomosaics from 2019, DEMs from 2021, as well as three of the orthomosaics from 2021, have been uploaded to dataset 1 of 2 (DOI: 10.5281/zenodo.7105133).  For reference, this dataset includes the orthomosaics produced from the following days in July 2021 (in separate files):  1) 8th 2) 9th 3) 10th 4) 11th 5) 12th 6) 15th

opencc-by-4.0Sep 2022View details →

zenodo44/100

UAV-based imagery for road damage detection

This dataset contains a set of annotated examples of damage present to the road,  obtained via an UAV in Poznań, Poland. The imagery was obtained by flying a UAV over a set of roads at approximately 70m above ground level, and further creating an ortophotomosaics of three different pieces of road. Lastly, the ortophotomosaics were patched into non-overlapping patches of 512x512 pixel size, and each patch was manually labeled with a set of objects and classes proposed in the UAPD dataset (https://doi.org/10.1016/j.autcon.2021.103991, https://github.com/tantantetetao/UAPD-Pavement-Distress-Dataset). Patches without a significant presence of the road-like objects were discarded, and the data includes: <ul> <li>ortophotomosaics -- a set of three original ortophotomosaics with ground sampling distance of 2.54 cm/pixel in .tif format,</li> <li>labeled patches -- a set of 99 patches containing road with annotations in both PASCAL VOC (.xml) and YOLO (.txt) formats</li> </ul> Lastly, this data has been used as a holdout set in a Master's thesis conducted at Poznań University of Technology.

opencc-by-4.0Jun 2024View details →

zenodo44/100

UAV-based monocular SLAM video datasets in vineyards with RTK ground truth

The dataset provides a UAV-based monocular visual SLAM data, designed to evaluate the potential of using monocular visual SLAM in vineyards. It includes videos in ".mp4" format collected by UAV, and  "xlsx" tables which include latitude, longitude, height, speed in x, y and z, comjpass, pitch, roll. The ".xlsx" tables were measured by RTK and can be used as ground truth of UAV trajectory and pose. This dataset can be combined with other datasets to enable a comprehensive view of the vineyards: Vélez S, Ariza-Sentís M, Valente J. EscaYard: Precision viticulture multimodal dataset of vineyards affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics. Data in Brief. 2024 Jun 1;54:110497. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110497</a> Ariza-Sentís M, Wang K, Cao Z, Vélez S, Valente J. GrapeMOTS: UAV vineyard dataset with MOTS grape bunch annotations recorded from multiple perspectives for enhanced object detection and tracking. Data in Brief. 2024 Jun 1;54:110432. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110432" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110432</a>

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_17

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221023_SYC-aldabra-arm06_UAV-02_17 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 277 Median height: 70 meters Survey area: 389618.75 hectares Survey from: 2022:10:23 09:11:33 to: 2022:10:23 09:27:32 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221020 - 02_2

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221020 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221020_SYC-aldabra-arm01_UAV-02_2 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 266 Median height: 70 meters Survey area: 9.92 hectares Survey from: 2022:10:20 09:50:29 to: 2022:10:20 10:04:39 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221025 - 02_32

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221025 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221025_SYC-aldabra-arm01_UAV-02_32 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 261 Median height: 100 meters Survey area: 9.41 hectares Survey from: 2022:10:25 11:57:39 to: 2022:10:25 12:08:32 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra gigi, Seychelles - 20221024 - 02_31

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra gigi, Seychelles, on 20221024 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221024_SYC-aldabra-gigi_UAV-02_31 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -88.50 - Survey informations: No Images: 270 Median height: 148 meters Survey area: 65.52 hectares Survey from: 2022:10:24 16:49:25 to: 2022:10:24 17:04:45 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra passe dubois 40m, Seychelles - 20221024 - 02_23

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe dubois 40m, Seychelles, on 20221024 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221024_SYC-aldabra-passe-dubois-40m_UAV-02_23 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 291 Median height: 40 meters Survey area: 34485.25 hectares Survey from: 2022:10:24 08:50:06 to: 2022:10:24 09:11:28 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_18

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221023_SYC-aldabra-arm06_UAV-02_18 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 293 Median height: 70 meters Survey area: 25.55 hectares Survey from: 2022:10:23 09:35:54 to: 2022:10:23 09:52:58 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra passe dubois, Seychelles - 20221022 - 02_13

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe dubois, Seychelles, on 20221022 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221022_SYC-aldabra-passe-dubois_UAV-02_13 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 274 Median height: 70 meters Survey area: 13.87 hectares Survey from: 2022:10:22 09:22:29 to: 2022:10:22 09:33:17 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra gigi, Seychelles - 20221024 - 02_29

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra gigi, Seychelles, on 20221024 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221024_SYC-aldabra-gigi_UAV-02_29 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -90.00 - Survey informations: No Images: 325 Median height: 151 meters Survey area: 51.44 hectares Survey from: 2022:10:24 15:21:26 to: 2022:10:24 15:40:13 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra passe arm01, Seychelles - 20221024 - 02_28

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe arm01, Seychelles, on 20221024 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221024_SYC-aldabra-passe-arm01_UAV-02_28 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -75.00 - Survey informations: No Images: 241 Median height: 70 meters Survey area: 84339.72 hectares Survey from: 2022:10:24 13:18:01 to: 2022:10:24 13:34:24 "

opencc-by-4.0Jun 2024View details →

zenodo44/100

Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221021 - 02_7

"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221021 The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), - PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... Original tree structure: │ └─ 20221021_SYC-aldabra-arm01_UAV-02_7 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │-------- └─ PROCESSED_DATA Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: Average Camera Pitch: -75.00 - Survey informations: No Images: 119 Median height: 150 meters Survey area: 41.78 hectares Survey from: 2022:10:21 09:37:33 to: 2022:10:21 09:54:07 "

opencc-by-4.0Jun 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