PyGMTSAR: Sentinel-1 Python InSAR. An Introduction. Python InSAR, #1
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- FormatePub
- ISBN8223233565
- EAN9798223233565
- Date de parution01/07/2023
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurDraft2Digital
Résumé
Attention: The book describes a previous version of PyGMTSAR. While the core principles remain the same, the Google Colab and Docker image links refer to outdated examples. For the latest materials, please visit the author's Patreon at https://www.patreon.com/pechnikov. The "PyGMTSAR: Sentinel-1 Python InSAR" book series serves as your gateway to mastering the innovative world of Sentinel-1 satellite interferometry using the open-source Python InSAR library, PyGMTSAR.
Authored by the developer himself, these books act as hands-on guides for working with PyGMTSAR, whether through Jupyter notebooks or console Python scripts. The book "PyGMTSAR: Sentinel-1 Python InSAR. An Introduction" employs Google Colab, a free-to-use cloud service, as an ideal platform for beginners. Readers can explore the applications of PyGMTSAR, from seismic activity tracking to infrastructure health assessment, through a series of interactive notebooks.
Each notebook comes complete with adaptable instructions to facilitate personalized learning. The guide also introduces Docker Desktop, an advanced open-source platform for containerization. The PyGMTSAR Docker image sets up a workspace similar to a traditional one, enabling more intense computations on your local computer and on cloud hosts. All the Google Colab examples are available. This tutorial sheds light on the principles of lazy and delayed computations.
It explains how Dask, an advanced task scheduler, intelligently partitions and schedules tasks. These insights enhance your ability to handle Big Data processing with PyGMTSAR efficiently, whether on your local machine or cloud-based systems. Whether you're a student, a researcher, or an industry professional with an interest in remote sensing and earth observation, the "PyGMTSAR: Sentinel-1 Python InSAR.
An Introduction" book equips you with the necessary skills and knowledge to navigate Python-based satellite interferometry.
Authored by the developer himself, these books act as hands-on guides for working with PyGMTSAR, whether through Jupyter notebooks or console Python scripts. The book "PyGMTSAR: Sentinel-1 Python InSAR. An Introduction" employs Google Colab, a free-to-use cloud service, as an ideal platform for beginners. Readers can explore the applications of PyGMTSAR, from seismic activity tracking to infrastructure health assessment, through a series of interactive notebooks.
Each notebook comes complete with adaptable instructions to facilitate personalized learning. The guide also introduces Docker Desktop, an advanced open-source platform for containerization. The PyGMTSAR Docker image sets up a workspace similar to a traditional one, enabling more intense computations on your local computer and on cloud hosts. All the Google Colab examples are available. This tutorial sheds light on the principles of lazy and delayed computations.
It explains how Dask, an advanced task scheduler, intelligently partitions and schedules tasks. These insights enhance your ability to handle Big Data processing with PyGMTSAR efficiently, whether on your local machine or cloud-based systems. Whether you're a student, a researcher, or an industry professional with an interest in remote sensing and earth observation, the "PyGMTSAR: Sentinel-1 Python InSAR.
An Introduction" book equips you with the necessary skills and knowledge to navigate Python-based satellite interferometry.
Attention: The book describes a previous version of PyGMTSAR. While the core principles remain the same, the Google Colab and Docker image links refer to outdated examples. For the latest materials, please visit the author's Patreon at https://www.patreon.com/pechnikov. The "PyGMTSAR: Sentinel-1 Python InSAR" book series serves as your gateway to mastering the innovative world of Sentinel-1 satellite interferometry using the open-source Python InSAR library, PyGMTSAR.
Authored by the developer himself, these books act as hands-on guides for working with PyGMTSAR, whether through Jupyter notebooks or console Python scripts. The book "PyGMTSAR: Sentinel-1 Python InSAR. An Introduction" employs Google Colab, a free-to-use cloud service, as an ideal platform for beginners. Readers can explore the applications of PyGMTSAR, from seismic activity tracking to infrastructure health assessment, through a series of interactive notebooks.
Each notebook comes complete with adaptable instructions to facilitate personalized learning. The guide also introduces Docker Desktop, an advanced open-source platform for containerization. The PyGMTSAR Docker image sets up a workspace similar to a traditional one, enabling more intense computations on your local computer and on cloud hosts. All the Google Colab examples are available. This tutorial sheds light on the principles of lazy and delayed computations.
It explains how Dask, an advanced task scheduler, intelligently partitions and schedules tasks. These insights enhance your ability to handle Big Data processing with PyGMTSAR efficiently, whether on your local machine or cloud-based systems. Whether you're a student, a researcher, or an industry professional with an interest in remote sensing and earth observation, the "PyGMTSAR: Sentinel-1 Python InSAR.
An Introduction" book equips you with the necessary skills and knowledge to navigate Python-based satellite interferometry.
Authored by the developer himself, these books act as hands-on guides for working with PyGMTSAR, whether through Jupyter notebooks or console Python scripts. The book "PyGMTSAR: Sentinel-1 Python InSAR. An Introduction" employs Google Colab, a free-to-use cloud service, as an ideal platform for beginners. Readers can explore the applications of PyGMTSAR, from seismic activity tracking to infrastructure health assessment, through a series of interactive notebooks.
Each notebook comes complete with adaptable instructions to facilitate personalized learning. The guide also introduces Docker Desktop, an advanced open-source platform for containerization. The PyGMTSAR Docker image sets up a workspace similar to a traditional one, enabling more intense computations on your local computer and on cloud hosts. All the Google Colab examples are available. This tutorial sheds light on the principles of lazy and delayed computations.
It explains how Dask, an advanced task scheduler, intelligently partitions and schedules tasks. These insights enhance your ability to handle Big Data processing with PyGMTSAR efficiently, whether on your local machine or cloud-based systems. Whether you're a student, a researcher, or an industry professional with an interest in remote sensing and earth observation, the "PyGMTSAR: Sentinel-1 Python InSAR.
An Introduction" book equips you with the necessary skills and knowledge to navigate Python-based satellite interferometry.