Showing 9 of total 9 results (show query)
brazil-data-cube
rstac:Client Library for SpatioTemporal Asset Catalog
Provides functions to access, search and download spacetime earth observation data via SpatioTemporal Asset Catalog (STAC). This package supports the version 1.0.0 (and older) of the STAC specification (<https://github.com/radiantearth/stac-spec>). For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
Maintained by Felipe Carvalho. Last updated 9 months ago.
geospatialspatiotemporal-asset-catalogstac
20.6 match 72 stars 8.28 score 250 scripts 3 dependents
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 1 months ago.
big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogcpp
15.5 match 494 stars 9.50 score 384 scripts
rsetienne
DAISIE:Dynamical Assembly of Islands by Speciation, Immigration and Extinction
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.
Maintained by Rampal S. Etienne. Last updated 1 months ago.
7.9 match 9 stars 8.59 score 55 scripts 1 dependents
ropensci
rsi:Efficiently Retrieve and Process Satellite Imagery
Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'.
Maintained by Michael Mahoney. Last updated 2 months ago.
7.5 match 49 stars 7.20 score 38 scripts
appelmar
gdalcubes:Earth Observation Data Cubes from Satellite Image Collections
Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.
Maintained by Marius Appel. Last updated 1 years ago.
remote-sensingsatellite-imageryspatial-analysisgdalnetcdfcpp
1.8 match 124 stars 8.39 score 356 scripts
pepijn-devries
CopernicusMarine:Search Download and Handle Data from Copernicus Marine Service Information
Subset and download data from EU Copernicus Marine Service Information: <https://data.marine.copernicus.eu>. Import data on the oceans physical and biogeochemical state from Copernicus into R without the need of external software.
Maintained by Pepijn de Vries. Last updated 3 months ago.
1.7 match 25 stars 5.88 score 20 scripts 2 dependents
mlr-org
mlr3spatiotempcv:Spatiotemporal Resampling Methods for 'mlr3'
Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.
Maintained by Patrick Schratz. Last updated 4 months ago.
cross-validationmlr3resamplingresampling-methodsspatialtemporal
0.8 match 50 stars 8.09 score 123 scripts
