U4Py Documentation

This Python module was created in the framework of the project Umwelt 4.0 financed by the Hessian Agency for Nature Conservation, Environment and Geology. It is split into several packages:

• analysis: Does the data analysis, e.g., inversion or spatial analysis.

• plotting: Pre-made figures and axis objects for specific plots including formatting.

• utils: Various utility functions, e.g., file and project management.

• io: Tools to read various file formats.

• addons: Tools to read externally supplied data such as groundwater levels or weather data.

View the Full API Documentation here

Each module can be imported and used separately for creating individual workflows and plots.

Installation

Currently supported Python Version: 3.11., due to some packages being not available for 3.12, yet.

This module uses GDAL which cannot be installed from pypi for many operating systems. You can download precompiled binary wheels from here.

Some scripts use ADAtools by Barra et al. 2017, Tomás et al. 2019 and Navarro et al. 2020. If this functionality is needed, please contact one of the authors for a personal copy of ADAtools.

To install the module you need to use pip together with git:

python -m pip install git+https://git-ce.rwth-aachen.de/rudolf/u4py

This automatically installs the module u4py including all prerequisites to your Python environment.

Examples and Notebooks

Several standalone scripts and interactive Jupyter notebooks are available. Scripts and notebooks require u4py installed on your system. Additionally, all notebooks and some scripts (arcpy_*.py) require a valid installation of ArcGIS including an arcpy environment.

The scripts contain full workflows to do data preparation or processing. You can Download Example Scripts as zip files.

The notebooks contain the workflow to prepare the dataset for manual classification. You can Download Example Notebooks as zip files.

Source

You can find the source repository here:.

Indices and tables

• Overview of module

• Index of all functions

References

• 1. Barra, L. Solari, M. Béjar-Pizarro, O. Monserrat, S. Bianchini, G. Herrera, M. Crosetto, R. Sarro, A. G.E., R. Maria Mateos, S. Ligüerzana, C. López, S. Moretti, A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images , Remote Sensing, Vol. 9, No. 10, September 2017.

• 18. Tomás, J. Ignacio Pagán, J. A. Navarro, M. Cano, J. Luis Pastor, A. Riquelme, M. Cuevas-González, M. Crosetto, A. Barra, O. Monserrat, J. M. López-Sánchez, A. Ramón, S. Iborra, M. del Soldato, L. Solari, S. Bianchini, F. Raspini, F. Novali, A. Ferreti, M. Constantini, F. Trillo, G. Herrera, N. Casagli, Semi-Automatic Identification and Pre-Screening of Geological-Geotechnical Deformational Processes Using Persistent Scatterer Interferometry Datasets , Remote Sensing, Vol. 11, No. 14, July 2019.

• 10. 1. Navarro, R. Tomás, A, Barra, J. I. Pagán, C. Reyes-Carmona, L. Solari, J. L.Vinielles, S. Falco, M. Crosetto, ADAtools: Automatic Detection and Classification of Active Deformation Areas from PSI Displacement Maps. ISPRS Int. J. Geo-Inf. 2020, 9, 584.