Codes to measure the grain size distribution from thin sections or µXCT images. The distribution can be calculated based on the sieve method (volume-based), the laser diffraction (frequency-based) method, or the point-count method. The code outputs are (1) grain centroid - a matrix of Cartesian coordinates for each grain, (2) grain radius – a matrix of the radii measured from the grain centroid for each grain, (3) grain azimuth, - a matrix of the azimuth of all principal axes, (4) grain inclination – a matrix of the inclination of all principal axes, (5) grain volume – a vector of the volume in voxels for each grain, and (6) the number of contacts - a vector of the number of adjacent grains in contact.

stabix

Slip transfer analysis toolbox and GUIs (Matlab)

stac-api-spec

SpatioTemporal Asset Catalog API specification - an API to make geospatial assets openly searchable and crawlable

stamps

Stanford Method for Persistent Scatterers

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stanford_dl_ex

Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial

steady-state-flow-with-neural-nets

A Tensorflow re-implementation of the paper Convolutional Neural Networks for Steady Flow Approximation

stereo

Estimate a depth map from two or more images

stereopipeline

The NASA Ames Stereo Pipeline is a suite of automated geodesy & stereogrammetry tools designed for processing planetary imagery captured from orbiting and landed robotic explorers on other planets.