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154 results for “multispectral”
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>
A high-throughput multispectral imaging system for museum specimens
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Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
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MODID: Multispectral oral disease image dataset with segmentaion
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Data from: Evaluating UAV captured RGB and multispectral imagery as a proxy for visual rating of leaf spot in cultivated peanut
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Multitemporal multispectral imagery for rice yield and phenology prediction
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5cm multispectral imagery from UAV campaign at Niwot Ridge, 2017
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice_image analysis
<p>This dataset contains the HE staining/anti-CD31 staining/ICG fluorescence images and the image quantification data produced for publication "Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice"</p>
Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice
<p>This dataset contains primary data produced for pulication named 'Functional multispectral optoacoustic tomography imaging of hepatic steatosis development in mice'</p>
Laser welding multispectral coaxial monitoring
<p>This is the first dataset containing thermopgraphy data of some single laser welding trials as they were observed by coaxial integrated NIR and S/MWIR cameras and different IR filters.</p>
Supplementary material for "Classification of Eurasian Watermilfoil (Myriophyllum spicatum) Using Drone-enabled Multispectral Imagery Analysis" paper
<p>This material has the unsummarized versions of the water chemistry and light data collected for each site, by date. It also includes all error matrices for each classification. It also includes details of each classification site and result. </p>
Cloud-free multispectral Landsat geomedian of Mexico for the year 2011
<p>Cloud-free national image, generated with the Open Data Cube (https://www.opendatacube.org/) with 1177 Landsat 5 Scenes and 2362 Landsat 7 Scenes with a total of 90% of the scenes with Tier 1 quality from the year 2011 Summarized with the Geomedian Algorithm. In Cloud Optimized GeoTIFF (COG) format (https://www.cogeo.org/).</p>
Avian-eye-inspired perovskite artificial vision system for foveated and multispectral imaging
<p>Avian eyes possess a deep central fovea as a result of extensive evolution. Deep fovea efficiently refracts incident light, creating a magnified image of the target object and making it easier to track its motion. These features are essential for detecting and tracking remote objects in dynamic environments. Furthermore, avian eyes respond to a wide spectrum of light, including visible and ultraviolet light, allowing them to efficiently distinguish the target object from complex backgrounds. Despite notable advances in artificial vision systems that mimic animal vision, the exceptional object detection and targeting capabilities of avian eyes via foveated and multispectral imaging remain underexplored. Here, we present an artificial vision system that capitalizes on these aspects of avian vision. We introduce an artificial fovea and vertically-stacked perovskite photodetector arrays whose designs are optimized by theoretical simulations for the demonstration of foveated and multispectral imaging. The artificial vision system successfully identifies colored and mixed-color objects and detects remote objects through foveated imaging. The potential for use in uncrewed aerial vehicles that need to detect, track, and recognize distant targets in dynamic environments is also discussed. Our avian-eye-inspired perovskite artificial vision system marks a notable advance in bio-inspired artificial visions.</p>
Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery (dataset, paper published in PLOS ONE)
<p>Data processed to analyze pine processionary moth defoliation.</p> <p>Digital surface model and orthomosaics derived from UAS</p>
A Feasibility Study for the Use of Multispectral Optoacoustic Tomography in the Detection of Tumors
ClinicalTrials.gov study NCT04365413. IPD Sharing: Not stated. Countries: 1. Publications: 0.
New Imaging Biomarkers for Muscular Diseases - Multispectral Optoacoustic Imaging in Spinal Muscular Atrophy
ClinicalTrials.gov study NCT04115475. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture
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Avian-eye-inspired perovskite artificial vision system for foveated and multispectral imaging
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Multispectral Radiometry percent reflectance:Natural Enemies, Plant Diversity and Plant Community Composition
The purpose of this experiment is to determine the influences of natural enemies, including plant pathogenic fungi and insect pests, influence plant community composition, productivity, and diversity over time. The experiment is being conducted in a subset of plots within the Big Biodiversity field, including monoculture, 2-species, 4-species, 8-species, 16-species, and 32-species plots. There are 5 different treatments: foliar fungicide, soil drench fungicide, foliar insecticide, the combination of all pesticides, and nontreated control. The pesticides are applied repeatedly throughout the growing season. Within the plots, community productivity, species composition, percent cover, and pest damage are being quantified over time.
Multispectral and augmented Landsat data with land cover labels
<p>Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503</p> <p>The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923.</p> <p>The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library).</p> <p>The labeled classes correspond to the following identifier.</p> <p>1, Temperate Coniferous forest<br> 2, Temperate Decidius Forest<br> 3, Temperate Mixed Forest<br> 4, Tropical Evergreen Forest<br> 5, Tropical Deciduous Forest<br> 6, Scrubland<br> 7, Wetland Vegetation<br> 8, Agriculture<br> 9, Grassland<br> 10, Water body<br> 11, Barren Land<br> 12, Urban Area</p> <p>To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html).</p> <p>The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip.<br> Later, a transformation to Albers equal-area conic projection was done with the followings parameters:</p> <p>Fake east: 2500000.0<br> Fake North: 0.0<br> Origin longitude: -102.0º<br> Origin latitude: 12.0º<br> First standard parallel: 17.5º<br> Second standard parallel: 29.5º<br> Linear unit: Meter (1.0)<br> Reference ellipsoid: GRS80</p> <p><br> Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation.</p> <p>Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized.<br> Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated.</p> <p>A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class.<br> Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected.</p> <p>Image patches were extracted from the selected regions in the sample.</p> <p>The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5.</p> <p>From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km.</p> <p>The 102 channels in the patches correspond to:</p> <p>Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2<br> Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi<br> Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth</p> <p>2011 Landsat Time Analysis Series by Pixel</p> <p>(red-swir 1)/(red+swir 1); (5): min, mean, max, std, median<br> (red-nir)/( red+nir); (5): min, mean, max, std, median<br> (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median<br> (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median<br> (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median<br> (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median<br> (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median<br> (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median<br> (green-red)/(green+red); (5): min, mean, max, std, median<br> (green-nir)/(green+nir); (5): min, mean, max, std, median<br> (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median<br> (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median<br> (blue-red)/(blue+red); (5): min, mean, max, std, median<br> (blue-nir)/(blue+nir); (5): min, mean, max, std, median<br> (blue-green)/( blue+green); (5): min, mean, max, std, median</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.