Other

项目介绍

luhipi/sarvey 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 Other 许可证,目前获得 96 个 Star、25 次 Fork。

仓库全名
luhipi/sarvey
开源许可证
Other
Star 数
96
Fork 数
25
未关闭 Issue
20

README 摘要

======================== SARvey - survey with SAR ======================== Open-source InSAR time series analysis software developed within the project SAR4Infra. SARvey aims to analyze InSAR displacement time series for engineering applications. Documentation The documentation with installation instructions, processing steps, and examples with a demo dataset can be found at: https://luhipi.github.io/sarvey/main…

完整内容见 GitHub 原始 README 或本页 。

常见问题

sarvey 用什么语言开发?

主要使用 Python 编写,采用 Other 许可证,目前有 96 个 Star。

sarvey 可以免费商用吗?

仓库标注的许可证为 Other,商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 sarvey 的源码?

可以直接克隆仓库:git clone https://github.com/luhipi/sarvey.git,默认分支为 main;安装与运行步骤见仓库 README。

sarvey 最近还在维护吗?

仓库最近一次更新为 2 周前,当前有 20 个未关闭 Issue;Star 数为 96,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

Open-source research software for InSAR time series analysis

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

简要分析

热度分
152
价值分
64
活跃状态
活跃

语言占比

Makefile
Python
Shell

Release

README

======================== SARvey - survey with SAR

Open-source InSAR time series analysis software developed within the project SAR4Infra. SARvey aims to analyze InSAR displacement time series for engineering applications.

Documentation

The documentation with installation instructions, processing steps, and examples with a demo dataset can be found at: https://luhipi.github.io/sarvey/main

Discussion

Use the Q&A in discussion_ to ask questions and get help from the community. Report bugs and request features in the issue_ tracker.

Status

… image:: https://github.com/luhipi/sarvey/actions/workflows/ci.yml/badge.svg :target: https://github.com/luhipi/sarvey/actions :alt: Pipelines … image:: https://img.shields.io/static/v1?label=Documentation&message=GitHub Pages&color=blue :target: https://luhipi.github.io/sarvey/main :alt: Documentation … image:: https://zenodo.org/badge/DOI/10.5281/zenodo.12544130.svg :target: https://doi.org/10.5281/zenodo.12544130 :alt: DOI

License

SARvey is distributed under the GNU General Public License, version 3 (GPLv3).

The following exceptions applies:

This package uses PyMaxFlow. The core of PyMaxflows library is the C++ implementation by Vladimir Kolmogorov. It is also licensed under the GPL, but it REQUIRES that you cite [BOYKOV04] in any resulting publication if you use this code for research purposes. This requirement extends to SARvey.

Please check out the details of the license here <LICENSE>_.

How to cite

If you use SARvey in your research, please cite the following.

  1. The paper describing the methodology:

    Piter A, Haghshenas Haghighi M, Motagh M (2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92, 609-627. Link to paper <https://link.springer.com/article/10.1007/s41064-024-00314-x>_.

  2. The software itself. Please specify the version you use:

    Piter A, Haghshenas Haghighi M, FERN.Lab, Motagh M (2024). SARvey - survey with SAR [version]. Zenodo. https://doi.org/10.5281/zenodo.12544130

  3. If you use the PUMA method for unwrapping in your research, please cite the following publication as indicated in the license:

    Boykov Y, Kolmogorov V (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1124–1137, DOI 10.1109/TPAMI.2004.60. Link to paper <https://ieeexplore.ieee.org/document/1316848>_.

Processing overview

… image:: https://seafile.projekt.uni-hannover.de/f/006f702937cd4e618bcb/?dl=1 :width: 600 :align: center :alt: SARvey workflow

Processing workflow for using the SARvey software to derive displacement time series. SARvey builds upon and uses functions of the two InSAR research softwares MintPy_ (multilook SBAS processing) and MiaplPy_ (Phase linking for single-look DS processing).

SARvey is a command-line-based software. The major steps for running SARvey are the following:

  • Installation

    SARvey is a cross-platform python-based software and can be installed on Linux and MacOS. On Windows, SARvey is tested on Windows Subsystem for Linux (WSL_) version 2. Details of installation can be found in installation instruction_.

  • Preprocessing

    The software requires a coregistered stack of SLC and the related geometry information in the MiaplPy_ data format. The coregistered stack of SLC can be created using an InSAR processor. Currently MiaplPy_ only supports ISCE_. Support for GAMMA and SNAP_ is planned for future. After creating the coregistered stack of SLC, run the “load_data” step from Miaplpy_ to create the “inputs” directory which contains “slcStack.h5” and “geometryRadar.h5”. Details are explained in the preparation_ section

  • Time series analysis

    Time series analysis is performed using sarvey. It consists of 5 steps (steps 0 to 4). The details of each step are explained in processing steps_. The processing parameters are handled in a json config file. Visualization and export are handled by sarvey_plot and sarvey_export packages. Below are the major steps:

    • Go to your working directory:

      … code-block:: bash

       cd path/to/working_dir/
      
    • Create a default config file using “-g” flag:

      … code-block:: bash

       sarvey -f config.json -g
      
    • Modify config.json to change path to “inputs” directory. Modify other parameters as desired.

    • Run all processing steps (steps 0 to 4):

      … code-block:: bash

       sarvey -f config.json 0 4
      

      Different processing steps are explained in processing_ section.

    • Plot the resulting displacement time series:

      … code-block:: bash

       sarvey_plot outputs/p2_coh80_ts.h5 -t
      
    • Export the results as Shapefiles_:

      … code-block:: bash

       sarvey_export outputs/p2_coh80_ts.h5 -o outputs/shp/p2_coh80.shp
      

Feature overview

SARvey has three main components for processing, visualization, and exporting data.

  • sarvey performs time series analysis.
  • sarvey_plot plots the outputs.
  • sarvey_export exports InSAR time series results from to GIS data formats. The GIS data format can be visualized for example in QGIS_.

It also has two components that facilitate transport infrastructure monitoring.

  • sarvey_mask creates mask from Shapefiles, e.g. for transport infrastructures.
  • sarvey_osm downloads transport infrastructure information from OSM_ and store as Shapefiles.

You can run each component in the command line with “-h” argument for more information about the usage. For example:

… code-block:: bash

   sarvey -h

SARvey supports two processing schemes:

  • Two-step unwrapping_ with atmospheric correction (default).

  • One-step unwrapping_ for a small area.

History / Changelog

You can find the protocol of recent changes in the SARvey package history_.

We follow the principle of semantic versioning. The version number is structured as follows: MAJOR.MINOR.PATCH. You can find a description of the versioning scheme here <https://semver.org/>__.

Credits

This software was developed within the project SAR4Infra (2020-2024) with funds of the German Federal Ministry for Digital and Transport. The project consortium consists of the Institute of Photogrammetry and GeoInformation_ at Leibniz University Hannover, FERN.Lab_ (innovation and technology transfer lab of the GFZ German Research Centre for Geosciences, Potsdam), Landesamt fuer Vermessung und Geoinformation Schleswig-Holstein, and Landesbetrieb Strassenbau und Verkehr Schleswig-Holstein. The scientific and methodological development was carried out by Andreas Piter ([email protected]), supervised by Mahmud H. Haghighi ([email protected]) and Mahdi Motagh ([email protected]). The FERN.Lab_ ([email protected]) contributed to the development, documentation, continuous integration, and testing of the package.

This package was created with Cookiecutter_ and the fernlab/cookiecutter-python-package_ project template.

… _Cookiecutter: https://github.com/audreyr/cookiecutter … _fernlab/cookiecutter-python-package: https://git.gfz-potsdam.de/fernlab/products/cookiecutters/cookiecutter-python-package … _processing: https://luhipi.github.io/sarvey/main/processing.html … _processing steps: https://luhipi.github.io/sarvey/main/processing.html#processing-steps-for-two-step-unwrapping-workflow … _preparation: https://luhipi.github.io/sarvey/main/preparation.html … _Two-step unwrapping: https://luhipi.github.io/sarvey/main/processing.html#processing-steps-for-two-step-unwrapping-workflow … _One-step unwrapping: https://luhipi.github.io/sarvey/main/processing.html#processing-steps-for-one-step-unwrapping-workflow … _installation instruction: https://luhipi.github.io/sarvey/main/installation.html … _history: https://luhipi.github.io/sarvey/main/history.html … _MiaplPy: https://github.com/insarlab/MiaplPy … _MintPy: https://github.com/insarlab/MintPy … _ISCE: https://github.com/isce-framework/isce2 … _SNAP: https://step.esa.int/main/toolboxes/snap … _Shapefiles: https://doc.arcgis.com/en/arcgis-online/reference/shapefiles.htm … _QGIS: https://qgis.org/en/site/ … _InSAR Explorer: https://luhipi.github.io/insar-explorer/ … _OSM: https://www.openstreetmap.org/ … _WSL: https://learn.microsoft.com/en-us/windows/wsl/ … _FERN.Lab: https://fernlab.gfz-potsdam.de/ … _Institute of Photogrammetry and GeoInformation: https://www.ipi.uni-hannover.de/en/ … _Landesamt fuer Vermessung und Geoinformation Schleswig-Holstein: https://www.schleswig-holstein.de/DE/landesregierung/ministerien-behoerden/LVERMGEOSH/lvermgeosh_node.html … _Landesbetrieb Strassenbau und Verkehr Schleswig-Holstein: https://www.schleswig-holstein.de/DE/Landesregierung/LBVSH/lbvsh_node.html … _discussion: https://github.com/luhipi/sarvey/discussions … _issue: https://github.com/luhipi/sarvey/issues

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