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.
108
datasets available to search
ShareScore release 0.7.1
Dataset results
108 results for “imagery data”
Data from: Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery
Open the record for dataset details and reuse information.
Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?
Open the record for dataset details and reuse information.
Data from: Implicit violent imagery processing among fans and non-fans of violent music
Open the record for dataset details and reuse information.
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Open the record for dataset details and reuse information.
Data from: Modeling avian biodiversity using raw, unclassified satellite imagery
Open the record for dataset details and reuse information.
Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project
Open the record for dataset details and reuse information.
Data repository from Boudewijn van Lieshout' thesis A comparison between Normalized Difference Vegetation Indices calculated from Sentinel 2A satellite data and high-resolution UAV imagery in Tanzania
<p>Monitoring vegetation is imperative for policy design and efficiency measurements. Frequent data collection, easy and inexpensive accessibility of images and the possibility of large area analysis, makes it still valuable to use satellite imagery. This study aims to examine how a Normalized Difference Vegetation Index (NDVI) measured with Sentinel-2A satellite data relates to an NDVI from Unmanned Aerial Vehicles (UAV henceforth) in study areas Chamwino Mlimwa and Chemba Waida, Tanzania.</p>
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index
India has one of the largest assemblages of tropical biodiversity, with its unique floristic composition of endemic species. However, current forest cover assessment is performed via satellite-based forest surveys, which have many limitations. The present study, which was performed in the Eastern Ghats, analysed the satellite-based inventory provided by forest surveys and inferred from the results that this process no longer provides adequate information for quantifying forest degradation in an empirical manner. The study analysed 21 soil properties and generated a forest soil quality index of the Eastern Ghats, using principal component analysis. Using matrix modules and geospatial technology, we compared the forest degradation status calculated from satellite-based forest surveys with the degradation status calculated from the forest soil quality index. The Forest Survey of India classified about 1.8% of the Eastern Ghats' total area as degraded forests and the remainder (98.2%) as open, dense, and very dense forests, whereas the soil quality index results found that about 42.4% of the total area is degraded, with the remainder (57.6%) being non-degraded. Our ground truth verification analyses indicate that the forest soil quality index along with the forest cover density data from the Forest Survey of India are ideal tools for evaluating forest degradation.
Data from: Differential neural processing during motor imagery of daily activities in chronic low back pain patients
Chronic low back pain (chronic LBP) is both debilitating for patients but also a major burden on the health care system. Previous studies reported various maladaptive structural and functional changes among chronic LBP patients on spine- and supraspinal levels including behavioral alterations. However, evidence for cortical reorganization in the sensorimotor system of chronic LBP patients is scarce. Motor Imagery (MI) is suitable for investigating the cortical sensorimotor network as it serves as a proxy for motor execution. Our aim was to investigate differential MI-driven cortical processing in chronic LBP compared to healthy controls (HC) by means of functional magnetic resonance imaging (fMRI). Twenty-nine subjects (15 chronic LBP patients, 14 HC) were included in the current study. MI stimuli consisted of randomly presented video clips showing every-day activities involving different whole-body movements as well as walking on even ground and walking downstairs and upstairs. Guided by the video clips, subjects had to perform MI of these activities, subsequently rating the vividness of their MI performance. Brain activity analysis revealed that chronic LBP patients exhibited significantly reduced activity compared to HC subjects in MI-related brain regions, namely the left supplementary motor area and right superior temporal sulcus. Furthermore, psycho-physiological-interaction analysis yielded significantly enhanced functional connectivity (FC) between various MI-associated brain regions in chronic LBP patients indicating diffuse and non-specific changes in FC. Current results demonstrate initial findings about differences in MI-driven cortical processing in chronic LBP pointing towards reorganization processes in the sensorimotor network.
Supplementary material 1 from: Martin-Cabrera P, Perez Perez R, Irrison J-O, Lombard F, Ove Möller K, Rühl S, Creach V, Lindh M, Stemmann L, Schepers L (2022) Establishing Plankton Imagery Dataflows Towards International Biodiversity Data Aggregators. Biodiversity Information Science and Standards 6: e94196. https://doi.org/10.3897/biss.6.94196
Imagery dataset example
Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning
<p><strong>Paper Abstract:</strong></p> <p>Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R<sup>2</sup>: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R<sup>2</sup>: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R<sup>2</sup>: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R<sup>2</sup>: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper. </p>
Pinus (Pinus radiata) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =4) of Pinus radiata acquired between 2016-2017 in Chile. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 3-4 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Pinus cover. 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 Pinus canopies 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>Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. <em>Remote sensing of environment</em>, <em>227</em>, 61-73.</p> <p><a href="https://doi.org/10.1016/j.rse.2019.03.025">https://doi.org/10.1016/j.rse.2019.03.025</a></p> <p><a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166">https://www.sciencedirect.com/science/article/abs/pii/S0034425719301166</a></p>
Spekboom (Portulacaria afra) UAV imagery and reference data (raw)
<p>This dataset includes drone (Uncrewed Aerial Vehicles, UAV) orthomosaics (RGB, n =32) of Spekboom (Portulacaria afra) acquired between 2020-21 in South Africa. The resolution (ground sampling distance) of the orthomosaics amounts to approx. 1 cm. The orthomosaics are partially labelled (polygon shapefiles) in terms of Spekboom cover. 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 Spekboom canopies 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:<br> Galuszynski, N. C., Duker, R., Potts, A. J., & Kattenborn, T. (2022). Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery. <em>PeerJ</em>, <em>10</em>, e14219.</p> <p><a href="https://doi.org/10.7717/peerj.14219">https://doi.org/10.7717/peerj.14219</a></p> <p><a href="https://peerj.com/articles/14219/">https://peerj.com/articles/14219/</a></p>
Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)
<p>Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)</p>
Data accompanying the article "Mapping Antarctic Crevasses and their Evolution with Deep Learning Applied to Satellite Radar Imagery"
<p>Fracture and backscatter maps from June 2021 at 100m resolution, covering the Antarctic Ice Sheet.</p> <p>Maps showing estimated change in fracture density between January 2015 and July 2022 covering the Amundsen Sea Sector of West Antarctica, and accompanying uncertainty estimates at 1km resolution.</p> <p>Each of these four datasets is in GeoTiff form with CRS: EPSG:3031 - WGS 84 / Antarctic Polar Stereographic</p> <p> </p>
High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data
Open the record for dataset details and reuse information.
Data from: Critical analysis of forest degradation in the southern Eastern Ghats of India: comparison of satellite imagery and soil quality index
Open the record for dataset details and reuse information.
Data from: Differential neural processing during motor imagery of daily activities in chronic low back pain patients
Open the record for dataset details and reuse information.
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
Open the record for dataset details and reuse information.
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.