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Dataset results
10 results for “UAV image dataset”
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
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>
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-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>
UAV thermal image dataset
<p>The thermal images dataset is captured by DJI Matrice 210 Drone equipped with FLIR thermal camera for SAR operation.</p>
Unmanned Aerial Vehicle (UAV) image dataset.
<p>The dataset contains 2,919 images and separated into five classes of car, taxi, truck, bus and motorcycle.<br> </p>
Our processed LoveDA dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed LoveDA dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>"</p>
Our processed CITY_OSM dataset for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our processed CITY_OSM dataset is used for the paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
A dataset of aerial images taken by UAV that we collected for "LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images"
<p>Our private dataset of UAV aerial imagery for paper "<a href="https://doi.org/10.7717/peerj-cs.1467">LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images</a>".</p>
ScienceDex guides
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OpenNeuro
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