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120 results for “UAV images”
CoFly-WeedDB: A UAV image dataset for weed detection and species identification
<p>The CoFly-WeedDB contains 201 RGB images (~436MB) from the attached camera of DJI Phantom Pro 4 from a cotton field in Larissa, Greece during the first stages of plant growth. The RGB images were collected while the Unmanned Aerial Vehicle (UAV) was performing a coverage mission over the field's area. During the designed mission, the camera angle was adjusted to -87°, vertically with the field. The flight altitude and speed of the UAV were equal to 5m and 3m/s, respectively, aiming to provide a close and clear view of the weed instances. All images have been annotated by expert agronomists using the LabelMe annotation tool, providing the exact boundaries of 3 types of common weeds in this type of crop, namely (i) Johnson grass, (ii) Field bindweed, and (iii) Purslane. The dataset can be used alone and in combination with other datasets to develop AI-based methodologies for automatic weed segmentation and classification purposes.</p>
Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
UAV Railroad Images for Segmentation and Obstacle Detection
<p><span>UAV-RSOD dataset contains five different types of obstacles including </span><span>person, boulder, barrel, branch, jerry can, and iron rod. This dataset supports the development and evaluation of computer vision models for railroad-related applications, such as autonomous train navigation, obstacle detection in railroad, railroad extraction and safety monitoring. </span><span> </span><span>The DJI Phantom 4 PRO UAV </span><span>Drone was used to take all the videos and images in the Indian Railways, Tiruchirapalli city of Tamilnadu, India.</span></p> <p><span>Version 1: 315 Images with Labelling, Masking for Segmentation Process and without any Data Augmentation techniques applied</span></p> <p><span>Version 2: 2002 Annotated Images with Data Augmentation techniques applied (Rotate, Flip, etc) for Obstacle Detection</span></p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
UAV RAW images from UAV at 50m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV RAW images from UAV at 50m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV RAW images from DJI Mini 2 UAV at 50m altitude in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, Ria De Vigo, coastal area, raw drone images, DJI raw images</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> jpg</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
UAV RAW images from UAV at 80m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV RAW images from UAV at 80m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV RAW images from DJI Mini 2 UAV at 80m altitude in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, Ria De Vigo, coastal area, raw drone images, DJI raw images</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> jpg</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
Dataset on UAV High-resolution Images from Grassland with Broad-leaved Dock (Rumex Obtusifolius)
<p>The dataset consists of orthophotos (build from UAV images) from a grassland field in which several <em>Rumex obtusifolius</em> plants were detected. The field is located in Germany (Kleve). The UAV images were acquired at 10, 15, and 30 meters height. Moreover, the labels/annotations from the <em>Rumex obtusifolius</em> plants in the images are also provided. </p>
UAV RGB imagery dataset captured at nadir and oblique angles over pistachio trees in Spain, including images, GCPs, 3D point cloud and orthomosaic.
<p>The dataset comprises 248 images taken in two flights on 29 July 2021 over a pistachio orchard in Spain. In addition, GCPs (ground control points) were collected to improve the photogrammetric process accuracy. The photos were taken using a UAV DJI Phantom Advance quadcopter equipped with a DJI FC6310 RGB 20-megapixel camera. The first flight mission was planned to take nadir images (-90º gimbal pitch degree), whereas the second flight was scheduled to take oblique images (-60º gimbal pitch degree), both at 55 metres above the ground. In addition, the images were used to generate a 3D point cloud, DEM and orthomosaic, which were included in the dataset.This dataset is useful for precision agriculture researchers interested in photogrammetric reconstruction.</p>
UAV survey images of the eastern Endalen slope, Svalbard
<p>A UAV survey of the eastern hillslope in <a href="https://toposvalbard.npolar.no/?lat=78.17844&long=15.75892&zoom=9&layer=aerial">Endalen, Svalbard</a>. The survey consists of 428 post-processed drone images in a tif format, taken by a DJI Mavic 2 Pro on 8 August 2022. The post-processing consisted of applying regular photographic contrast enhancement and noise reduction procedures in darktable version 4.4.0. The exact filters used are available in the EXIF metadata.</p> <p>This is a supplementary dataset for Hamm et al., 2025: <a href="https://doi.org/10.5194/tc-19-3693-2025">https://doi.org/10.5194/tc-19-3693-2025</a></p>
UAV-based multispectral image data of a tree nursery, Eberswalde, Brandenburg, 2023 (Orthomosaics, DEMs, point clouds)
<p>This data set contains three multispectral surveys conducted with an unoccupied aerial vehicle (UAV DJI M300 RTK) of an oak tree nursery experiment</p> <ul> <li>Date of acquisition: 16.08.2023</li> <li>Location: Tree nursery, Eberswalde, Brandenburg, Germany</li> <li>UAV: DJI M300 RTK with active SAPOS connection</li> <li>Flight altitude above ground level: 30 m, 40 m, 60 m</li> <li>Image Overlap forward/side: 80 % / 80 %</li> <li>Camera: MicaSense Altum multispectral</li> <li>EPSG: 32632</li> </ul> <p>Data products: </p> <ul> <li>Orthomosaic multispectral (Resolution: 1.35 cm, 1.79 cm, 2.65 cm)</li> <li>Orthomosaic RGB</li> <li>Orthomosaic thermal LWIR in pseudo °C according to <a href="https://support.micasense.com/hc/en-us/articles/360022446473-Converting-Altum-Thermal-to-degrees-C-after-processing-in-Agisoft-or-Pix4D">Micasense</a> empirical formula</li> <li>DEM</li> <li>Dense point cloud RGB</li> <li>Spectral indices calculated: NDVI, NDRE</li> <li>Agisoft Report</li> </ul> <p><strong>Acknowledgment:</strong></p> <p><strong>Frank Becker</strong></p> <p>Landesbetrieb Forst Brandenburg</p> <p>Landeskompetenzzentrum Forst Eberswalde (LFE)</p>
SSIMS-Flow: UAV image velocimetry workbench - example video #1
<p>Example video #1 for <strong>SSIMS-Flow: UAV image velocimetry workbench</strong> tool (<a href="https://github.com/ljubicicrobert/SSIMS-Flow">Official repository</a>).</p>
Manually mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains manually mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Mapping has been done in ArcGIS with snapping toggled on to document the topological abutment relationships between traces.</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in manual mapping was done as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the years 2019 and 2020.</p> <p>This dataset has been previously used as part of a Master's Thesis, which can be located here: <a href="https://urn.fi/URN:NBN:fi-fe202003259211">https://urn.fi/URN:NBN:fi-fe202003259211</a>, and in a Journal of Structural Geology article (OG1 target area), located here: https://doi.org/10.1016/j.jsg.2021.104304.</p>
Automatically mapped traces from UAV-acquired images of Loviisa shoreline outcrops
<p>This dataset contains automatically mapped two-dimensional bedrock fracture traces from shoreline outcrop orthomosaics generated from UAV-acquired images (https://doi.org/10.5281/zenodo.7077518). The outcrops are located along the shoreline of Loviisa, South-East Finland. Code for automatic mapping is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as ESRI Shapefiles in the ETRS-TM35FIN (EPSG:3067) coordinate system. </p> <p>The work in automatic mapping was done as part of a Geological Survey of<br> Finland project, Kallioperän Rikkonaisuus, during 2021-2022.</p>
Rocktopo Sensing Mountains Summer School 2024: UAV images, pointcloud and TLS pointclouds
<p>This dataset contains high-resolution 3D scans of an outdoor rock climbing wall, captured using both <strong>Unmanned Aerial Vehicle (UAV)</strong> photogrammetry and <strong>Terrestrial Laser Scanning (TLS)</strong>. The dataset includes images from the UAV, the reconstructed point cloud derived from photogrammetry, and the point cloud from TLS. These scans were conducted on the following dates:</p> <p>The used TLS was a Trimble X7 and the UAV a DJI Spark.</p> <ul> <li> <p><strong>TLS (Terrestrial Laser Scanning)</strong>: Provided in <code>TLS_240924</code> or <code>TLS_240925</code>, this folder contains the point cloud data captured via TLS, offering highly accurate and detailed 3D geometry of the climbing wall surface. In LAS format.</p> </li> <li> <p><strong>UAV Images: </strong>Stored in <code>UAV_240924</code> or <code>UAV_240925</code>, these subdirectories contain a set of aerial images taken from the UAV, which were later used to reconstruct the 3D point cloud of the rock climbing wall. In JPEG format.</p> </li> <li> <p><strong>Photogrammetric Point Cloud (LAS)</strong>: The resulting 3D point cloud from UAV photogrammetry is stored in <code>LAS_240924</code> or <code>LAS_240925</code>. These files represent the spatial data and geometry derived from processing the UAV images using photogrammetry techniques. In LAS format.</p> </li> <li><strong>Aligned Point Cloud (ICP)</strong>: The ICP aligned clouds that were used for analysis are stored in <code>ICP</code>. These files represent the spatial data and geometry derived from aligning the TLS and UAV clouds. In LAS format.</li> <li><strong>Potree Point Cloud (folder)</strong>: Octree converted pointclouds used in the <a href="https://github.com/jurriandoorbos/potree-rocktopo" target="_blank" rel="noopener">Potree rocktopo</a> visualization.</li> <li><strong>Route Points</strong>: Polylines of every route in the lower location, in .poly (x y z) format.</li> </ul> <p>The combination of both photogrammetric and laser scanning data provides a comprehensive and high-fidelity 3D representation of the climbing wall, useful for geospatial analysis, surface reconstruction, and outdoor modeling applications. The pointclouds are not georeferenced.</p> <p><strong>Please note: </strong>the TLS and UAV were taken simultaneously: the same location is therefore:<strong> UAV/LAS_240924 fits with TLS_250924</strong> and vice-versa.</p> <p> </p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
UAV Images of Road Vehicles
<p>This annotated dataset contains five different types of vehicles: cars, taxis, trucks, buses, and motorcycles taken from Unmanned Aerial Vehicles (UAVs), which we commonly know as drones. Mavic Air 2 Drone was used to take all the pictures in the Iraqi cities of Sulaimaniyah and Erbil.</p> <p>Version 1 with Data Augmentation techniques applied (Brightness, Hue, and Noise)</p> <p>Version 2 without any Data Augmentation techniques applied.</p> <p> </p> <p> </p>
Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity
<p>This is data for the paper "Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity". (<strong>DOI: </strong><a href="https://doi.org/10.1002/esp.5665">https://doi.org/10.1002/esp.5665</a>)</p> <p>This data is openly available, provided the original work is properly cited. The unzip Password can be found on the original paper in ESPL.</p>
UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images
<p>The images in the dataset ( VOC format) were captured by a UAV at an altitude of 30 meters. The collected images were annotated in PASCAL VOC format. A total of 11,158 instances in 2,440 images are incorporated in the dataset.</p> <ul> <li>The UAV-PDD2023 dataset, captured by unmanned aerial vehicles (UAVs), provides a benchmark for road damage detection. It is highly useful for municipal authorities and road agencies to conduct low-cost road condition monitoring. </li> <li>Six types of road damages are labeled in the dataset: Longitudinal cracks (LC), Transverse cracks (TC), Alligator cracks (AC), Oblique cracks (OC), Repair (RP), and Potholes (PH). </li> <li>Researchers can use this dataset as a benchmark to evaluate the performance of different algorithms in addressing similar problems, such as image classification and object detection. </li> </ul> <p> </p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
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