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I have to apologize for the long hiatus. I am busy finishing my PhD thesis, and some time-demanding projects at work.
However, as promised, I have managed to continue the StaMPS-workflow documentation. I think that StaMPS is quite demanding for inexperienced people (mostly students starting to work with PSI) who
I am currently involved in several projects that rely on Persistent Scatterer Interferometry (PSI), a radar-based technique that belongs to the group of differential interferometric Synthetic Aperture Radar (SAR).
Specifically, I am using StaMPS/MTI to analyse Sentinel-1 SLC data. StaMPS/MTI written in Matlab and C++. Currently, this seems to
Today I am going to show you how to perform a very basic kMeans unsupervised classification of satellite imagery using R. We will do this on a small subset of a Sentinel-2 image.
Sentinel-2 is a satellite launched by the European Space Agency and its data is freely accessible for
today I’d like to point to the Rocker project, which provides a suite of Docker images for particular tasks. Even though the Rocker initiative already exists since 2014 and I read about it once in a while, there were not many use cases for me to try it
we’re back for round two of PSI processing with StaMPS:
A GeoJSON file (Simple Feature Access standard) describing the region of interest is needed. The easiest way to create a ROI polygon is by converting an existing Layer into a bounding box in QGIS (Vector / Research
For current cause I’m posting about the GeoPackage file format today.
Some of you may have asked yourself: ‘Why should I use GeoPackage?’, ‘Is GeoPackage better than Esri Shapefile?’ or even: ‘What is GeoPackage?!’
back in 2014, Hadley Wickham’s
dplyr tutorial at useR!2014 drew a lot of attention to the
%>% (pipe) operator from the
magrittr package. While the pipe operator is an essential part of the tidyverse workflow, and is thus well-known to users of packages belonging to the umbrella of