A Python module of a fast and intelligent algorithm for finding the pixel displacement between two images autoRIFT can be installed as a standalone Python module (does not support radar coordinates) where both manual and conda installs (https://github.com/conda-forge/autorift-feedstock) are supported or with the InSAR Scientific Computing Environment (ISCE: https://github.com/isce-framework/isce2) software that supports handling Cartesian and radar coordinates Use cases include all dense feature tracking applications, including the measurement of surface displacements occurring between two repeat satellite images as a result of glacier flow, large earthquake displacements, and land slides autoRIFT can be used for dense feature tracking between two images over a grid defined in an arbitrary geographic-coordinate projection when used in combination with the sister Geogrid Python module (https://github.com/leiyangleon/Geogrid). Example applications include searching radar-coordinate imagery on a polar stereographic grid and searching Universal Transverse Mercator (UTM) imagery at a specified geographic-coordinate grid Copyright (C) 2019 California Institute of Technology. Government Sponsorship Acknowledged. Link: https://github.com/leiyangleon/autoRIFT Acknowledgement: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (https://its-live.jpl.nasa.gov/) and through Alex Gardner’s participation in the NASA NISAR Science Team

In this update, changes have been made to handle both GDAL 2 and GDAL 3, and a few bugs were also fixed.