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>. 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, and methods for active learning and uncertainty assessment. Supports object-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.

Last updated 6 months ago

big-earth-datacbersearth-observationeo-datacubesgeospatialimage-time-seriesland-cover-classificationlandsatplanetary-computerr-spatialremote-sensingrspatialsatellite-image-time-seriessatellite-imagerysentinel-2stac-apistac-catalogopenblascppopenmp

9.52 score 489 stars 376 scripts 450 downloads

torchopt - Advanced Optimizers for Torch

Optimizers for 'torch' deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in 'torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019), <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021) <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019), <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021), <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al. (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018), <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

Last updated 2 years ago

deep-learningnumerical-optimization

4.59 score 26 stars 15 scripts 191 downloads

bayesEO - Bayesian Smoothing of Remote Sensing Image Classification

A Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels.

Last updated 8 months ago

openblascppopenmp

3.60 score 4 stars 6 scripts 237 downloads

Rwtss - Client for Web Time-Series Service

Allows remote access to satellite image time series provided by the web time series service (WTSS) available at servers such as <https://brazildatacube.dpi.inpe.br/wtss/>. The functions include listing the data sets available in WTSS servers, describing the contents of a data set, and retrieving a time series based on spatial location and temporal filters.

Last updated 3 years ago

2.28 score 19 scripts 193 downloads