Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
51
datasets available to search
ShareScore release 0.9.0
Dataset results
51 results for “Aerial Images”
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>
PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images
<p>We release two datasets we used in the paper "PSMNet-FusionX3: LiDAR-Guided Deep Learning Stereo Dense Matching on Aerial Images". These two datasets are from aerial images and LiDAR, the detailed information can be found on Github : https://github.com/whuwuteng/PSMNet-FusionX3. These datasets can be also used for Deep learning stereo-dense matching.</p>
A global data set of realized treelines sampled from Google Earth aerial images
Open the record for dataset details and reuse information.
Aerial images and video records of the Kodor and Bzyp river plumes
<p>Aerial images and video records of the Kodor and Bzyp river plumes that illustrate their various dynamical features</p>
Craters in Historical Aerial Images (CHAI) Dataset
<p>This dataset contains 99 aerial images from Austria and Germany from 1943 - 1945. There are three versions of the dataset: <strong>CHAI-raw</strong>, <strong>CHAI-full</strong>, and <strong>CHAI-light</strong>. The CHAI-raw contains the original 99 historical aerial images with a Region of Interest (ROI) mask as well as the crater annotations. CHAI-full and CHAI-light are the derived datasets that were used for the evaluation of the paper <strong>"CHAI: Craters in Historical Aerial Images"</strong> presented at <a href="https://wacv2024.thecvf.com/">WACV2024</a>. For both datasets we extracted 960×960 patches with an overlap of 20%, both come with the images in .png format and the train, val, and test as .json files in the COCO style. The difference between the CHAI-full and CHAI-light datasets is that for the light version, all patches without any annotations have been removed, which results in the same amount of annotations, but fewer patches.</p><h2>Technical Details</h2><p>CHAI-raw contains all images unnormalized, additionally, the -info.csv contains the GSD in m, the -mask.png contains the region of interest (only in which craters were annotated), as well as the craters.csv and craters-manual-adapted.csv, which contain the craters and the manually refined craters, details can be found in the original publication. For ease of use, please consider using the derived datasets, which were used for the evaluation of our paper:<br><br>Will be linked once published.</p><p>Please cite the WACV paper when publishing results on these datasets.</p><h2>Access</h2><p>If you would like to request access to these files, please fill out the form below.</p><p>You need to satisfy these conditions in order for this request to be accepted:</p><p>The dataset is freely available for non-commercial research use. In order to get access to the dataset you have to fill in and sign the <a href="https://cvl.tuwien.ac.at/wp-content/uploads/2023/12/Form-Agreement-for-Usage-of-CHAI-Dataset.pdf">usage agreement form</a> and send it to <a href="https://cvl.tuwien.ac.at/staff/marvin-burges/">Marvin Burges</a><a href="mailto:sebastian.zambanini@tuwien.ac.at">.</a></p>
Bottom-of-atmosphere reflectance data from aerial imaging for Lake Mulargia (Sardinia, Italy) (2020/09/24)
<p>This dataset contains the surface reflectance Hyspex images derived with ATCOR code by CNR of Lake Mulargia (Sardinia, Italy). The acquisition was done by CGR Spa (Italy).</p>
Liveability from aerial images
<p>Dataset accompanying our paper<a href="http://10.1016/j.rse.2023.113454"> on monitoring the liveability of cities from overhead images in the Netherlands</a>. Our dataset combines liveability information based on surveyed resident opinions and hedonic pricing with aerial overhead images. A total of 51,781 liveability grid cells covering 100 by 100 meters are paired with aerial images of 1 meter pixel resolution with 500 by 500 pixels. Patches are distributed across 13 built-up areas of varying sizes and historical backgrounds. The Leefbaarometer 2.0 labels are <a href="https://www.leefbaarometer.nl/page/Open%20data">made available</a> by the Dutch Ministry of Internal Affairs (CC0), and the aerial images are <a href="https://data.overheid.nl/dataset/16186-luchtfoto-2021-hr-rgb-open-data">made available</a> by the Dutch cadastral services (CC BY 4.0)</p> <p>In our future work we will make time series data available, therefore enabling the longitudinal monitoring of urban liveability from overhead imagery on an unprecedented scale.</p> <p>The code to work with the dataset can be found in the <a href="https://github.com/ahlevering/liveability-rs">GitHub repository</a> accompanying the paper.</p>
Fusion of Single and Integral Multispectral Aerial Images
<p>We present a novel hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images and integral aerial images that result from synthetic aperture sensing for removing occlusion caused by dense vegetation. It combines the environment's spatial references with features of unoccuded targets. Our method out-beats the state-of-the-art, does not require manually tuned parameters, can be extended to an arbitrary number and combinations of spectral channels, and is reconfigurable to address different use-cases. </p>
Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat
<p>It contains supplementary materials(revised version)and supporting data of Tables of Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat.<em> </em>However, the artical has not published. Data is available upon request.</p> <p>If you need anything, please don't hesitate to contact me(liujk@ahstu.edu.cn).</p>
Initial camera matrix values for AWI CANON aerial imaging system
<p>The file contains a camera matrix in Agisoft Metashape's format that can be used as initial parameters for a multi view reconstruction of aerial sea ice images recorded with the AWI CANON 14mm wide-angle camera (sensor.awi.de ID 4430). </p>
UAV Aerial videos and paper Original images
<p>The original image file contains 13 videos and original image data captured by drones in the air.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.
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.
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.
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.