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Dataset results
11 results for “Optical imagery”
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery
<p>This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.</p>
Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping
<p>Sentinel-2 Optical satellite imagery for Epidemic Disease Mapping</p>
Using optical flow temporal interpolation of satellite imagery to assist multi-sensor global cloud product composites
Open the record for dataset details and reuse information.
Nares Strait Optical Imagery Sea Ice Observations
<p>Optical satellite imagery from acquired in the Nares Strait between from May to August over a 14-year period for Modis and a seven-year period for Sentinel-2. Clear days after the ice breakup within the Nares Strait were selected to ensure individual fragments could be identified. Images were collected between May and August 2016-2023.</p>
Detection of slow-moving landslides from Sentinel-2 optical satellite imagery
<p>Datasets and code associated with recent ESPL publication</p> <p>"Detection of slow-moving landslides through automated satellite monitoring of surface deformation"</p> <p>The three imagery folders are zipped for convenience. The full code is included in the one .m file.</p> <p>Please get in touch with any questions.</p>
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>
Hurricane and Severe Storm Sentinel (HS3) Global Modeling and Assimilation Office (GMAO) Dust Aerosol Optical Thickness Imagery V1
The Hurricane and Severe Storm Sentinel (HS3) Global Modeling and Assimilation Office (GMAO) Dust Aerosol Optical Thickness Imagery dataset consists of browse only imagery showing dust aerosol optical thickness and wind speed/direction from the Goddard Earth Observing System Model, version 5 (GEOS-5). These data are used to see how the Saharan Air Layer (SAL) affects hurricane development during the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environmental and storm-scale internal processes, addressing the controversial role of the SAL in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The browse only data files are available for dates between August 11, 2014 and October 5, 2014 at 3-hour intervals in PNG format.
High Mountain Asia 8-meter DEMs Derived from Along-track Optical Imagery V001
This data set contains 8-meter Digital Elevation Models (DEMs) of high mountain Asia glacier and snow regions generated from very-high-resolution commercial stereoscopic satellite imagery.
High Mountain Asia 8-meter DEMs Derived from Cross-track Optical Imagery V001
This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from from very-high-resolution commercial stereo satellite imagery.
High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery V001
This data set contains 8-meter Digital Elevation Model (DEM) mosaics of high mountain Asia glacier and snow regions generated from very-high-resolution (VHR) commercial satellite imagery.
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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.