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108 results for “imagery data”
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
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Data from: Early detection of encroaching woody Juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms
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Data from: The music of silence. Part I: Responses to musical imagery encode melodic expectations and acoustics
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Data and code for: A novel method for mapping high-precision animal locations using high-resolution imagery
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Data for: Non-invasive measurements of respiration and heart rate across wildlife species using Eulerian Video Magnification of infrared thermal imagery
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Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data
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Evapotranspiration data from eddy-covariance flux-tower measurements and Landsat imagery in California’s Sierra Nevada from 1985 to 2019
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MODIS/ASTER (MASTER) imagery and derived data in select neighborhoods of the greater Phoenix metropolitan area
A data collection campaign using the MODIS/ASTER airborne simulator (MASTER) was conducted in the greater Phoenix metropolitan area in July 2011 to collect visible through mid-infrared multispectral imagery. High resolution (7 m/pixel) land surface temperature products for day and night periods were calculated using the mid-infrared bands of data; surface reflectance, albedo, and Normalized Difference Vegetation Index (NDVI) products were calculated using the visible through shortwave infrared band data for 41 select neighborhoods. While the full MASTER dataset has been processed to at-sensor radiance, it did not include native geolocation data. As georeferencing the entire dataset was not possible with funds available, the processed data described above were extracted for the 41 spatially discrete Phoenix Area Social Survey neighborhoods within the MASTER flight boundary.
Data from: Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery
Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to: i) map vegetation cover using sub-meter spatial resolution (0.3 m) imagery; ii) quantify the amount of residential and non-residential vegetation; and iii) investigate the relationship between patterns of urban vegetation versus socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green, and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical cities.
Data from: Modeling avian biodiversity using raw, unclassified satellite imagery
Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.
Data from: Implicit violent imagery processing among fans and non-fans of violent music
It is suggested that long-term exposure to violent media may decrease sensitivity to depictions of violence. However, it is unknown whether persistent exposure to music with violent themes affects implicit violent imagery processing. Using a binocular rivalry paradigm, we investigated whether the presence of violent music influences conscious awareness of violent imagery among fans and non-fans of such music. Thirty-two fans and 48 non-fans participated in the study. Violent and neutral pictures were simultaneously presented one to each eye, and participants indicated which picture they perceived (i.e. violent percept, neutral percept, or blend of two) via key presses, while they heard Western popular music with lyrics that expressed happiness or Western extreme-metal music with lyrics that expressed violence. We found both fans and non-fans of violent music exhibited a general negativity bias for violent imagery over neutral imagery regardless of the music genres. For non-fans, this bias was stronger while listening to music that expressed violence than while listening to music that expressed happiness. For fans of violent music, however, the bias was the same while listening to music that expressed either violence or happiness. We discussed these results in view of current debates on the impact of violent media.
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Yield data from field measurements and satellite imagery from Sentinel-2 for three consecutive years
<p>Data From: Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F. Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques. <em>Remote Sensing</em>. 2019; 11(23):2873. https://doi.org/10.3390/rs11232873</p> <ul> <li>Yield values as point data</li> <li>Interpolated kriging yield data</li> <li>Satellite imagery</li> </ul>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project
Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machi ne learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</p>
Plant succession at Mueller Glacier (New Zealand) UAV imagery and reference data (raw)
<p>This dataset includes a drone (Uncrewed Aerial Vehicles, UAV) orthomosaic (RGB) of plant communities acquired at the Mueller Glacier (New Zealand) in Februrary 2018. The resolution (ground sampling distance) of the orthomosaic amounts to approx. 3-4 cm. The orthomosaic is partially labelled (polygon shapefiles) in terms of plant community cover. The plant communities have been defined according to a field survey (see reference below). The orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI plant communities are assumed to be completely delineated (by visual interpretation).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publications:</p> <p>Kattenborn, T., Eichel, J., & Fassnacht, F. E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. <em>Scientific reports</em>, <em>9</em>(1), 1-9.</p> <p><a href="https://doi.org/10.1038/s41598-019-53797-9">https://doi.org/10.1038/s41598-019-53797-9</a></p> <p><a href="https://www.nature.com/articles/s41598-019-53797-9">https://www.nature.com/articles/s41598-019-53797-9</a><br><br><br>Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., & Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. <em>Remote Sensing in Ecology and Conservation</em>, <em>6</em>(4), 472-486.</p> <p><a href="https://doi.org/10.1002/rse2.146">https://doi.org/10.1002/rse2.146</a></p> <p><a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146</a></p>
Knotweed (Reynoutria japonica) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =3) of Reynoutria japonica (Knotweed) acquired in 2021 in Germany. The resolution (ground sampling distance) of the orthomosaics is below 1 cm. The orthomosaics are labelled (polygon shapefiles) in terms of Knotweedcover. Each orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI Knotweedcanopies are assumed to be completely delineated (by visual interpretation).</p> <p>For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution.</p> <p>Details on the dataset are mentioned in the corresponding publication:</p> <p>Soltani, S., Feilhauer, H., Duker, R., & Kattenborn, T. (2022). Transfer learning from citizen science photographs enables plant species identification in UAVs imagery. <em>ISPRS Open Journal of Photogrammetry and Remote Sensing</em>, 100016.</p> <p><a href="https://doi.org/10.1016/j.ophoto.2022.100016">https://doi.org/10.1016/j.ophoto.2022.100016</a></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S2667393222000059">https://www.sciencedirect.com/science/article/pii/S2667393222000059</a></p>
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (02/04/2023 – 02/08/2023) ARM Data Center [Dataset]. DOI: 10.5439/1969041. <a href="http://arm.gov/capabilities/instruments/camspec-air">https://arm.gov/capabilities/instruments/camspec-air</a> , 2023.</p>
PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR)
<p>This data set contains high-resolution image products (orthomosaics) acquired from midsized uncrewed aerial systems that have been processed for value-added quality. The instrument itself, a multispectral imager, the Altum by Micasense, captures six spectral bands (red, green blue, NIR, red edge, and LWIR/thermal1) as radiance, which is converted to reflectance. The code used to develop these images first uses tools from the Micasense Python library2 to apply dark level corrections, row gradient corrections, and radiometric corrections. Next, it uses the processing API from Agisoft Metashape software to align and mosaic the processed imagery, following the processes developed by the USGS' structure from motion workflow documentation.3 Captures at different altitudes (recorded in MSL) produce an orthomosaic, a tif image containing information related to the six spectral bands, and a digital elevation model (DEM), a tif image containing information related to the elevation of the surveyed terraine. Metadata included in every image can be used to extract lat, lon, and reflectance values.</p> <p> </p> <p>Cite as: Tagestad, J., Nelson, K., Goldberger, L., Gonzalez-Hirshfeld, I. PI Data: Multispectral and Thermal Surface Imagery and Surface Elevation Mosaics (CAMSPEC-AIR). (07/09/2022 – 07/18/2022) ARM Data Center [Dataset]. DOI: 10.5439/1962600. https://arm.gov/capabilities/instruments/camspec-air , 2023.</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.