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108 results for “satellite imagery”

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zenodo36/100

Frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery

Results of high-resolution (pixel size 10–15 m) visible satellite imagery analysis, described in Bradtke and Herman 2023 "Spatial characteristics of frazil streaks in the Terra Nova Bay Polynya from high-resolution visible satellite imagery" (https://tc.copernicus.org/articles/17/2073/2023/). The source data for analysis came from three satellite sensors: ALI (Advanced Land Imager), OLI (Operational Land Imager), and MSI (Multispectral Instrument).  The dataset includes: <ol> <li>results of frazil streaks detection in polynya, ice_water (ice =1, water=2, NoData=0)</li> <li>maps of spatially averaged characteristics of ice in polynya (NoData = -100): <ul> <li>ice concentration, Cfs (-),</li> <li>frazil streaks orientation, Thetafs (degrees clockwise from the north)</li> <li>width of frazil streaks, Wfs (m)</li> </ul> </li> <li>maps of spatially averaged wind-wave characteristics obtained from the Fourier analysis (NoData = -100): <ul> <li>peak wave length, Lpeak (m)</li> <li>peak wave direction, Thetapeak (degrees clockwise from the north)</li> </ul> </li> </ol> Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758), in raster grid with 10m resolution, in GeoTIFFformat. File name convention is: sensor_YYYYMMDD_variable.tif

opencc-by-4.0Dec 2023View details →

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Heron Island Satellite Imagery Classified Benthic Data and Halo Analyses and Models

This dataset includes satellite imagery data from Heron Island, Australia downloaded from Google Earth Pro in 2023, with image from Maxar Technologies dated 2016 clipped to the shallow lagoon layer from the Allen Coral Atlas shape file classified into benthic categories: corals, algae and sand using a combination of unsupervised machine learning spectral classification and manual training and assignment of classes. This dataset also includes scoring of selected coral patch reefs for isolated halos across time using historical aerial imagery. We also include 2 notebooks with code used to generate figures and run analyses for data, geometric, and consumer-resource models for coral halo patterns supporting the work entitled, "Consumer-resource interactions reflected in coral halo patterns" by the authors listed. A knitted html for R Markdown file is also included.

openJan 2023View details →

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Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Sep 2024View details →

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Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

Power Generation Data Set This data set contains imaging data acquired by ESA's Sentinel-2 Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates   provided by the European Pollutant Release and Transfer Register [2]. The images contain scenes of power stations, some of which are actively emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. Description   Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground) . This repository contains a total of 2131 images. This repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. Content The following files are contained in this repository: <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul>      Acknowledgement If you use this data set, please cite our publication:     Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,     "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",     Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.   Author Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch References   [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2 [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial [3]: https://labelstud.io/ [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show [5]: https://doi.org/10.5281/zenodo.3574566

opencc-by-4.0Nov 2021View details →

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Supplementary datasets for: "A major destabilization structure revealed by 22 years of satellite imagery at Piton de la Fournaise"; Dumont et al.; Nature Communications

Example datasets for: "A major destabilization structure revealed by 22 years of satellite imagery at Piton de la Fournaise" Quentin Dumont, Valérie Cayol, Jean-Luc Froger, Aline Peltier In Nature Communications It contains input dataset files to launch inversion of intrusion-induced displacement at Piton de la Fournaise. Each folder in the archive provide files needed to launch inversion of intrusions belonging to each intrusive zone: - 2015/07 for the Main NE-SE intrusive zone (NE-SE_RZ_example_2015_07) - 2020/09 for the sill intrusive zone (Sill_example_2020_09) - 2019/06 for the N60 intrusive zone (N60_RZ_example_2019_06) - 2014/06 for the N120 intrusive zone (N120_RZ_example_2014_06) - 2003/09 for the N210 intrusive zone (N210_RZ_example_2003_09) - 2018/07 for the N300 intrusive zone (N300_RZ_example_2018_07)

opencc-by-4.0Jan 2022View details →

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Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.

Doodleverse/Segmentation Zoo UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset. These UNet model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data 1. NDWI (1 band): (g-nir)/(g+nir) 2. MNDWI (1 band): (swir-g)/(swir+g) 3. RGB (3 band): red, green, blue 4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes   These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. '.h5' weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model   References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Feb 2022View details →

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Segmentation Zoo Res-UNet models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset.

Doodleverse/Segmentation Zoo models for Landsat-8 satellite imagery, Coast Train v1 Landsat-8 4-class subset. These model data are based on the Coast Train v1 Landsat-8 labeled imagery subset. Models have been fitted to 4 different types of data 1. NDWI (1 band): (g-nir)/(g+nir) 2. MNDWI (1 band): (swir-g)/(swir+g) 3. RGB (3 band): red, green, blue 4. RGB-NIR-SWIR (5 band): red, green, blue, nir, swir Classes are: {0: water, 1: whitewater, 2:sediment, 3:other}. These classes have been remapped from the original 11 classes   These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files: 1. config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.   2. weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function  `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images   3. model card file: this is a json file containing the following fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata   References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Feb 2022View details →

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MMFlood: A Multimodal Dataset for Flood Delineation from Satellite Imagery

MMFlood is remote sensing dataset derived from Sentinel-1 (VV-VH), MapZen (DEM) and OpenStreetMap (Hydrography). It provides a complete and well-rounded set of data specifically designed for flood events, focusing on three main features: worldwide distribution, manually validated annotations and multiple modalities. <ul> <li>1748 pairs of SAR images and pixel-level annotations exclusively based around flooded areas and taking into account 95 flood events in 42 different countries around the world, in a time span ranging from 2014 to 2021.</li> <li>Image size <2000x2000, image resolution of 20m .</li> <li>Manually verified flood events across the world with raw SAR acquisitions derived from Sentinel-1.</li> <li>Includion of Digital Elevation Model (DEM) images.</li> </ul>

opencc-by-4.0May 2022View details →

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Satellite-derived chlorophyll-a concentrations for Lake Mulargia (Sardinia, Italy) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Mulargia (Sardinia, Italy) for the period 29 Mar. 2013 - 31 Jan. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

opencc-by-4.0Jul 2022View details →

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Fig. 4 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 4. Response curves, representing the dependence of predicted suitability both on the

opencc-by-4.0Aug 2022View details →

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Fig. 1 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria

Fig. 1. Satellite view of the study area. White circles represent the location of the colonies

opencc-by-4.0Aug 2022View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering in visible satellite imagery

Look-up tables for rayleigh scattering correction of satellite imager data in the visible spectral range. Derived from LibRadTran simulations for various (aerosol free) standard atmospheres.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (antarctic) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (antarctic) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and antarctic aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental average) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental average) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of rayleigh scattering and continental average aerosol composition (Hess et al., 1998) within satelite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (urban) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (urban) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and urban aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (rural) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (rural) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and rural aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine tropical) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine tropical aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine polluted) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine polluted) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine polluted aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental polluted) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (continental polluted) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and continental polluted aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine clean) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (marine clean) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and marine clean aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range.

opencc-by-4.0Sep 2017View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record