Showing 4 of total 4 results (show query)
e-sensing
sits:Satellite Image Time Series Analysis for Earth Observation Data Cubes
An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Includes methods to reduce training samples imbalance proposed by Chawla et al (2002) <doi:10.1613/jair.953>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference as described by Camara et al (2024) <doi:10.3390/rs16234572>, and methods for active learning and uncertainty assessment. Supports region-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
Maintained by Gilberto Camara. Last updated 2 months ago.
big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp
494 stars 9.50 score 384 scripts
martinschobben
oceanexplorer:Explore Our Planet's Oceans with NOAA
Provides tools for easy exploration of the world ocean atlas of the US agency National Oceanic and Atmospheric Administration (NOAA). It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.
Maintained by Martin Schobben. Last updated 1 years ago.
earthearth-observationearth-sciencenoaanoaa-dataoceanocean-sciencesoceanographyopen-datashinyspatialspatial-analysisspatial-data
9 stars 5.01 score 23 scripts
zivankaraman
CDSE:'Copernicus Data Space Ecosystem' API Wrapper
Provides interface to the 'Copernicus Data Space Ecosystem' API <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available data from Copernicus Sentinel missions and obtaining the images for just the area of interest based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various satellite imagery archives. It allows you to access raw satellite data, rendered images, statistical analysis, and other features. This package is in no way officially related to or endorsed by Copernicus.
Maintained by Zivan Karaman. Last updated 2 months ago.
copernicusearth-observationremote-sensing
15 stars 4.91 score 12 scripts
xavi-rp
LPDynR:Land Productivity Dynamics Indicator
It uses 'phenological' and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.
Maintained by Xavier Rotllan-Puig. Last updated 7 months ago.
copernicus-global-land-serviceearth-observationland-degradationland-productivityvegetation
8 stars 4.90 score 5 scripts