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59 results for “Image sensing”
Beat Pilot Tone (BPT): Simultaneous MR Imaging and RF Motion Sensing at Arbitrary Frequencies
<p>This data is for the manuscript "Beat Pilot Tone (BPT): Simultaneous MR Imaging and Radio-Frequency Motion Sensing at Arbitrary Frequencies" submitted to the journal Magnetic Resonance in Medicine. It is divided into one subfolder for each figure, with each folder containing the data necessary to reproduce the figure. Most of the data are saved in "cfl" format as used by the BART Computational Magnetic Resonance Imaging Toolbox (https://mrirecon.github.io/bart/). Other data are saved as Comma Separated Values (csv) or plain text files.</p> <p>Keywords and Categories: Magnetic Resonance Imaging, MRI, medical imaging, radio frequency, microwave, motion sensing, motion correction</p>
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Muti-type Aircraft of Remote Sensing Images: MTARSI
<p>MTARSI has a total of 9'385 remote sensing images acquired from Google Earth satellite imagery and manually expanded, including 20~different types of aircraft covering 36~airports.<br> The new data set is made up of the following 20 aircraft types: B-1, B-2, B-29, B-52, Boeing, C-130, C-135, C-17, C-5, E-3, F-16, F-22, KC-10, C-21, U-2, A-10, A-26, P-63, T-6, T-43.<br> All the sample images are carefully labeled by seven specialists in the field of remote sensing images interpretation. <br> Each image contains one and only one complete aircraft.</p>
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></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>
Multi-type Aircraft of Remote Sensing Images: MTARSI 2
<p>Multi-Type Aircraft of Remote Sensing Images (MTARSI 2) dataset of aircraft on runways. The dataset has had some reclassification into 42 classifications, and extra data augmentation in those classifications. It is an example of an unbalanced dataset, with challenges of different light and viewing angles. Originated from https://zenodo.org/record/3464319#.YNwk3-hKiUk. (MTARSI)</p>
Reconstructed Aneto glacier surfaces from historic aerial image photogrammetry (1981) and remote sensing techniques (2020, 2021, 2022)
<p>The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. </p>
Multi-topography dataset for wind turbine detection from remote sensing image
<p>The land remote sensing wind turbine dataset has 1270 remote sensing images and contains 4459 individual wind turbines. The images are taken over a large time span and contain remote sensing images of the same wind farm at different times. The dataset has both YOLO and VOC tagging formats. The dataset can be divided into five categories based on the land background: sandy land, forest land, grassland, snow land, and wasteland. Rich land background can improve the robustness of the model, and different marker formats and a large number of wind turbine individuals can meet the object detection of different models. Affected by the size of different power wind turbines, the multi-angle imaging characteristics of remote sensing satellites, the different solar radiation angles in different seasons and vegetation shading, wind turbines show large differences in the images. The image features of the wind turbine shadow are more obvious than those of the wind turbine body, so in order for the detection model to better identify the wind turbine, we label the wind turbine body and the wind turbine shadow as a whole when using the labelImg tool for labeling the wind turbine target.</p>
Ca2+ imaging data for: A rigidity transition of MEC-2/Stomatin condensates controls neuronal mechanotransduction during touch sensing
<p>Calcium imaging data from Sanfeliu et al, NCB, 2023.</p> <p>Project contains fluorescence data to characterize the activity of the touch receptor neurons (TRNs) - which are mechanically activated upon touch – in the model organism <em>Caenorhabditis elegans</em>. The set of data includes wild-type animals as a reference and two different mutants to understand their role during touch sensation: MEC-2(R385H) and UNC-89 knock-out.</p> <p> </p>
Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
Open the record for dataset details and reuse information.
multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images
<p>Dataset for Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images from https://github.com/WindVChen/Push-and-Pull-Network?tab=readme-ov-file</p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
Dataset for "Fiber-Seismometer Hybrid Sensing for Seismic Imaging and Monitoring"
<p>The ambient noise dataset was collected on May 8, 2023, from a fiber-seismometer hybrid sensing deployment positioned along the Qiantang River in Hangzhou. The DAS data, Z-component and R-component data of seismometers are all stored in MAT format. Please refer to our study for detailed information on the dataset.</p> <p>Abstract about this study:</p> <p>Extreme climate events and geological disasters have intensified the urgency for advancing seismic imaging and monitoring. Despite developments in seismic instrumentation, particularly with seismometers and Distributed Acoustic Sensing (DAS), fine-scale observations remain challenging due to their inherent limitations and deployment configurations. This study introduces a novel hybrid sensing interferometry method that enhances multi-component signal extraction—especially poor horizontal components—through a two-step cross-correlation of DAS and seismometers. A field application near the Qiantang River in Hangzhou illustrates how our proposed framework retrieves high-quality multi-component empirical Green’s functions and advances ultra-short duration ambient noise seismic imaging techniques, including surface wave dispersion measurements and horizontal-to-vertical spectral ratio assessments. Our approach also facilitates monitoring of near-surface seismic velocity changes, dv/v, with an unprecedented 10-minute resolution, shedding light on shallow dynamic hydraulic responses. This innovative hybrid sensing framework offers new perspectives and methodologies for transforming future research in seismological observation, imaging, and monitoring. </p>
Experimental Data for: Machine learning enabled image analysis of time-temperature sensing colloidal arrays
<p>This dataset contains images of colloidal arrays functioning as time-temperature integrating, autonomous sensors. Each image shows a sensor consisting of multiple colloidal crystals with varying compositions of particles with different glass transition temperatures. Details on the composition and manufacturing procedures are explained in the corresponding publication. The data is organized into folders with different temperature setpoints. For each temperature, we investigated 10 samples. The name of the samples corresponds to their creation date. The name of the image files corresponds to their acquisition time. The first image in each sample folder was taken at the very start of the heating period. Hence, the heating time can be calculated by subtracting the start time from the acquisition time.</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
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.