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Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience / / by Wengang Zhang, Yanmei Zhang, Xin Gu, Chongzhi Wu, Liang Han
| Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience / / by Wengang Zhang, Yanmei Zhang, Xin Gu, Chongzhi Wu, Liang Han |
| Autore | Zhang Wen'gang |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (143 pages) |
| Disciplina | 550.2855133 |
| Collana | Engineering Series |
| Soggetto topico |
Engineering geology
Artificial intelligence Civil engineering Geoengineering Artificial Intelligence Civil Engineering |
| ISBN |
981-16-6834-5
981-16-6835-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Methods -- Applications -- Future Applications. |
| Record Nr. | UNINA-9910743396403321 |
Zhang Wen'gang
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Earth observation using Python : a practical programming guide / / Rebekah Bradley Esmaili
| Earth observation using Python : a practical programming guide / / Rebekah Bradley Esmaili |
| Autore | Esmaili Rebekah Bradley |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : AGU : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (300 pages) |
| Disciplina | 550.2855133 |
| Collana | Special publications series |
| Soggetto topico | Earth sciences - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-60691-8
1-119-60689-6 1-119-60692-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- Introduction -- Part I Overview of Satellite Datasets -- Chapter 1 A Tour of Current Satellite Missions and Products -- 1.1 History of Computational Scientific Visualization -- 1.2 Brief Catalog of Current Satellite Products -- 1.2.1 Meteorological and Atmospheric Science -- 1.2.2 Hydrology -- 1.2.3 Oceanography and Biogeosciences -- 1.2.4 Cryosphere -- 1.3 The Flow of Data from Satellites to Computer -- 1.4 Learning Using Real Data and Case Studies -- 1.5 Summary -- References -- Chapter 2 Overview of Python -- 2.1 Why Python? -- 2.2 Useful Packages for Remote Sensing Visualization -- 2.2.1 NumPy -- 2.2.2 Pandas -- 2.2.3 Matplotlib -- 2.2.4 netCDF4 and h5py -- 2.2.5 Cartopy -- 2.3 Maturing Packages -- 2.3.1 xarray -- 2.3.2 Dask -- 2.3.3 Iris -- 2.3.4 MetPy -- 2.3.5 cfgrib and eccodes -- 2.4 Summary -- References -- Chapter 3 A Deep Dive into Scientific Data Sets -- 3.1 Storage -- 3.1.1 Single Values -- 3.1.2 Arrays -- 3.2 Data Formats -- 3.2.1 Binary -- 3.2.2 Text -- 3.2.3 Self-Describing Data Formats -- 3.2.4 Table-Driven Formats -- 3.2.5 geoTIFF -- 3.3 Data Usage -- 3.3.1 Processing Levels -- 3.3.2 Product Maturity -- 3.3.3 Quality Control -- 3.3.4 Data Latency -- 3.3.5 Reprocessing -- 3.4 Summary -- References -- Part II Practical Python Tutorials for Remote Sensing -- Chapter 4 Practical Python Syntax -- 4.1 "Hello Earth" in Python -- 4.2 Variable Assignment and Arithmetic -- 4.3 Lists -- 4.4 Importing Packages -- 4.5 Array and Matrix Operations -- 4.6 Time Series Data -- 4.7 Loops -- 4.8 List Comprehensions -- 4.9 Functions -- 4.10 Dictionaries -- 4.11 Summary -- References -- Chapter 5 Importing Standard Earth Science Datasets -- 5.1 Text -- 5.2 NetCDF -- 5.2.1 Manually Creating a Mask Variable Using True and False Values.
5.2.2 Using NumPy Masked Arrays to Filter Automatically -- 5.3 HDF -- 5.4 GRIB2 -- 5.5 Importing Data Using Xarray -- 5.5.1 netCDF -- 5.5.2 Examining Vertical Cross Sections -- 5.5.3 Examining Horizontal Cross Sections -- 5.5.4 GRIB2 using Cfgrib -- 5.5.5 Accessing Datasets Using OpenDAP -- 5.6 Summary -- References -- Chapter 6 Plotting and Graphs for All -- 6.1 Univariate Plots -- 6.1.1 Histograms -- 6.1.2 Barplots -- 6.2 Two Variable Plots -- 6.2.1 Converting Data to a Time Series -- 6.2.2 Useful Plot Customizations -- 6.2.3 Scatter Plots -- 6.2.4 Line Plots -- 6.2.5 Adding Data to an Existing Plot -- 6.2.6 Plotting Two Side-by-Side Plots -- 6.2.7 Skew-T Log-P -- 6.3 Three Variable Plots -- 6.3.1 Filled Contour Plots -- 6.3.2 Mesh Plots -- 6.4 Summary -- References -- Chapter 7 Creating Effective and Functional Maps -- 7.1 Cartographic Projections -- 7.1.1 Geographic Coordinate Systems -- 7.1.2 Choosing a Projection -- 7.1.3 Some Common Projections -- 7.2 Cylindrical Maps -- 7.2.1 Global Plots -- 7.2.2 Changing Projections -- 7.2.3 Regional Plots -- 7.2.4 Swath Data -- 7.2.5 Quality Flag Filtering -- 7.3 Polar Stereographic Maps -- 7.4 Geostationary Maps -- 7.5 Creating Maps from Datasets Using OpenDAP -- 7.6 Summary -- References -- Chapter 8 Gridding Operations -- 8.1 Regular One-Dimensional Grids -- 8.2 Regular Two-Dimensional Grids -- 8.3 Irregular Two-Dimensional Grids -- 8.3.1 Resizing -- 8.3.2 Regridding -- 8.3.3 Resampling -- 8.4 Summary -- References -- Chapter 9 Meaningful Visuals through Data Combination -- 9.1 Spectral and Spatial Characteristics of Different Sensors -- 9.2 Normalized Difference Vegetation Index (NDVI) -- 9.3 Window Channels -- 9.4 RGB -- 9.4.1 True Color -- 9.4.2 Dust RGB -- 9.4.3. Fire/Natural RGB -- 9.5 Matching with Surface Observations -- 9.5.1 With User-Defined Functions -- 9.5.2 With Machine Learning. 9.6 Summary -- References -- Chapter 10 Exporting with Ease -- 10.1 Figures -- 10.2 Text Files -- 10.3 Pickling -- 10.4 NumPy Binary Files -- 10.5 NetCDF -- 10.5.1 Using netCDF4 to Create netCDF Files -- 10.5.2 Using Xarray to Create netCDF Files -- 10.5.3 Following Climate and Forecast (CF) Metadata Conventions -- 10.6 Summary -- Part III Effective Coding Practices -- Chapter 11 Developing a Workflow -- 11.1 Scripting with Python -- 11.1.1 Creating Scripts Using Text Editors -- 11.1.2 Creating Scripts from Jupyter Notebook -- 11.1.3 Running Python Scripts from the Command Line -- 11.1.4 Handling Output When Scripting -- 11.2 Version Control -- 11.2.1 Code Sharing though Online Repositories -- 11.2.2 Setting up on GitHub -- 11.3 Virtual Environments -- 11.3.1 Creating an Environment -- 11.3.2 Changing Environments from the Command Line -- 11.3.3 Changing Environments in Jupyter Notebook -- 11.4 Methods for Code Development -- 11.5 Summary -- References -- Chapter 12 Reproducible and Shareable Science -- 12.1 Clean Coding Techniques -- 12.1.1 Stylistic Conventions -- 12.1.2 Tools for Clean Code -- 12.2 Documentation -- 12.2.1 Comments and Docstrings -- 12.2.2 README File -- 12.2.3 Creating Useful Commit Messages -- 12.3 Licensing -- 12.4 Effective Visuals -- 12.4.1 Make a Statement -- 12.4.2 Undergo Revision -- 12.4.3 Are Accessible and Ethical -- 12.5 Summary -- References -- Conclusion -- Appendix A Installing Python -- A.1. Download Tutorials for This Book -- A.2. Download and Install Anaconda -- A.3. Package Management in Anaconda -- Appendix B Jupyter Notebook -- B.1. Running on a Local Machine (New Coders) -- B.2. Running on a Remote Server (Advanced) -- B.3. Tips for Advanced Users -- B.3.1. Customizing Notebooks with Configuration Files -- B.3.2. Starting and Ending Python Scripts -- B.3.3. Creating Git Commit Templates. Appendix C Additional Learning Resources -- Appendix D Tools -- D.1. Text Editors and IDEs -- D.2. Terminals -- Appendix E Finding, Accessing, and Downloading Satellite Datasets -- E.1. Ordering Data from NASA EarthData -- E.2. Ordering Data from NOAA/CLASS -- Appendix F Acronyms -- Index -- EULA. |
| Record Nr. | UNINA-9910554854903321 |
|
Esmaili Rebekah Bradley
|
||
| Hoboken, New Jersey : , : AGU : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Earth observation using Python : a practical programming guide / / Rebekah Bradley Esmaili
| Earth observation using Python : a practical programming guide / / Rebekah Bradley Esmaili |
| Autore | Esmaili Rebekah Bradley |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : AGU : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (300 pages) |
| Disciplina | 550.2855133 |
| Collana | Special publications series |
| Soggetto topico | Earth sciences - Data processing |
| ISBN |
1-119-60691-8
1-119-60689-6 1-119-60692-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- Introduction -- Part I Overview of Satellite Datasets -- Chapter 1 A Tour of Current Satellite Missions and Products -- 1.1 History of Computational Scientific Visualization -- 1.2 Brief Catalog of Current Satellite Products -- 1.2.1 Meteorological and Atmospheric Science -- 1.2.2 Hydrology -- 1.2.3 Oceanography and Biogeosciences -- 1.2.4 Cryosphere -- 1.3 The Flow of Data from Satellites to Computer -- 1.4 Learning Using Real Data and Case Studies -- 1.5 Summary -- References -- Chapter 2 Overview of Python -- 2.1 Why Python? -- 2.2 Useful Packages for Remote Sensing Visualization -- 2.2.1 NumPy -- 2.2.2 Pandas -- 2.2.3 Matplotlib -- 2.2.4 netCDF4 and h5py -- 2.2.5 Cartopy -- 2.3 Maturing Packages -- 2.3.1 xarray -- 2.3.2 Dask -- 2.3.3 Iris -- 2.3.4 MetPy -- 2.3.5 cfgrib and eccodes -- 2.4 Summary -- References -- Chapter 3 A Deep Dive into Scientific Data Sets -- 3.1 Storage -- 3.1.1 Single Values -- 3.1.2 Arrays -- 3.2 Data Formats -- 3.2.1 Binary -- 3.2.2 Text -- 3.2.3 Self-Describing Data Formats -- 3.2.4 Table-Driven Formats -- 3.2.5 geoTIFF -- 3.3 Data Usage -- 3.3.1 Processing Levels -- 3.3.2 Product Maturity -- 3.3.3 Quality Control -- 3.3.4 Data Latency -- 3.3.5 Reprocessing -- 3.4 Summary -- References -- Part II Practical Python Tutorials for Remote Sensing -- Chapter 4 Practical Python Syntax -- 4.1 "Hello Earth" in Python -- 4.2 Variable Assignment and Arithmetic -- 4.3 Lists -- 4.4 Importing Packages -- 4.5 Array and Matrix Operations -- 4.6 Time Series Data -- 4.7 Loops -- 4.8 List Comprehensions -- 4.9 Functions -- 4.10 Dictionaries -- 4.11 Summary -- References -- Chapter 5 Importing Standard Earth Science Datasets -- 5.1 Text -- 5.2 NetCDF -- 5.2.1 Manually Creating a Mask Variable Using True and False Values.
5.2.2 Using NumPy Masked Arrays to Filter Automatically -- 5.3 HDF -- 5.4 GRIB2 -- 5.5 Importing Data Using Xarray -- 5.5.1 netCDF -- 5.5.2 Examining Vertical Cross Sections -- 5.5.3 Examining Horizontal Cross Sections -- 5.5.4 GRIB2 using Cfgrib -- 5.5.5 Accessing Datasets Using OpenDAP -- 5.6 Summary -- References -- Chapter 6 Plotting and Graphs for All -- 6.1 Univariate Plots -- 6.1.1 Histograms -- 6.1.2 Barplots -- 6.2 Two Variable Plots -- 6.2.1 Converting Data to a Time Series -- 6.2.2 Useful Plot Customizations -- 6.2.3 Scatter Plots -- 6.2.4 Line Plots -- 6.2.5 Adding Data to an Existing Plot -- 6.2.6 Plotting Two Side-by-Side Plots -- 6.2.7 Skew-T Log-P -- 6.3 Three Variable Plots -- 6.3.1 Filled Contour Plots -- 6.3.2 Mesh Plots -- 6.4 Summary -- References -- Chapter 7 Creating Effective and Functional Maps -- 7.1 Cartographic Projections -- 7.1.1 Geographic Coordinate Systems -- 7.1.2 Choosing a Projection -- 7.1.3 Some Common Projections -- 7.2 Cylindrical Maps -- 7.2.1 Global Plots -- 7.2.2 Changing Projections -- 7.2.3 Regional Plots -- 7.2.4 Swath Data -- 7.2.5 Quality Flag Filtering -- 7.3 Polar Stereographic Maps -- 7.4 Geostationary Maps -- 7.5 Creating Maps from Datasets Using OpenDAP -- 7.6 Summary -- References -- Chapter 8 Gridding Operations -- 8.1 Regular One-Dimensional Grids -- 8.2 Regular Two-Dimensional Grids -- 8.3 Irregular Two-Dimensional Grids -- 8.3.1 Resizing -- 8.3.2 Regridding -- 8.3.3 Resampling -- 8.4 Summary -- References -- Chapter 9 Meaningful Visuals through Data Combination -- 9.1 Spectral and Spatial Characteristics of Different Sensors -- 9.2 Normalized Difference Vegetation Index (NDVI) -- 9.3 Window Channels -- 9.4 RGB -- 9.4.1 True Color -- 9.4.2 Dust RGB -- 9.4.3. Fire/Natural RGB -- 9.5 Matching with Surface Observations -- 9.5.1 With User-Defined Functions -- 9.5.2 With Machine Learning. 9.6 Summary -- References -- Chapter 10 Exporting with Ease -- 10.1 Figures -- 10.2 Text Files -- 10.3 Pickling -- 10.4 NumPy Binary Files -- 10.5 NetCDF -- 10.5.1 Using netCDF4 to Create netCDF Files -- 10.5.2 Using Xarray to Create netCDF Files -- 10.5.3 Following Climate and Forecast (CF) Metadata Conventions -- 10.6 Summary -- Part III Effective Coding Practices -- Chapter 11 Developing a Workflow -- 11.1 Scripting with Python -- 11.1.1 Creating Scripts Using Text Editors -- 11.1.2 Creating Scripts from Jupyter Notebook -- 11.1.3 Running Python Scripts from the Command Line -- 11.1.4 Handling Output When Scripting -- 11.2 Version Control -- 11.2.1 Code Sharing though Online Repositories -- 11.2.2 Setting up on GitHub -- 11.3 Virtual Environments -- 11.3.1 Creating an Environment -- 11.3.2 Changing Environments from the Command Line -- 11.3.3 Changing Environments in Jupyter Notebook -- 11.4 Methods for Code Development -- 11.5 Summary -- References -- Chapter 12 Reproducible and Shareable Science -- 12.1 Clean Coding Techniques -- 12.1.1 Stylistic Conventions -- 12.1.2 Tools for Clean Code -- 12.2 Documentation -- 12.2.1 Comments and Docstrings -- 12.2.2 README File -- 12.2.3 Creating Useful Commit Messages -- 12.3 Licensing -- 12.4 Effective Visuals -- 12.4.1 Make a Statement -- 12.4.2 Undergo Revision -- 12.4.3 Are Accessible and Ethical -- 12.5 Summary -- References -- Conclusion -- Appendix A Installing Python -- A.1. Download Tutorials for This Book -- A.2. Download and Install Anaconda -- A.3. Package Management in Anaconda -- Appendix B Jupyter Notebook -- B.1. Running on a Local Machine (New Coders) -- B.2. Running on a Remote Server (Advanced) -- B.3. Tips for Advanced Users -- B.3.1. Customizing Notebooks with Configuration Files -- B.3.2. Starting and Ending Python Scripts -- B.3.3. Creating Git Commit Templates. Appendix C Additional Learning Resources -- Appendix D Tools -- D.1. Text Editors and IDEs -- D.2. Terminals -- Appendix E Finding, Accessing, and Downloading Satellite Datasets -- E.1. Ordering Data from NASA EarthData -- E.2. Ordering Data from NOAA/CLASS -- Appendix F Acronyms -- Index -- EULA. |
| Record Nr. | UNINA-9910830429503321 |
|
Esmaili Rebekah Bradley
|
||
| Hoboken, New Jersey : , : AGU : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Python Recipes for Earth Sciences / / by Martin H. Trauth
| Python Recipes for Earth Sciences / / by Martin H. Trauth |
| Autore | Trauth Martin H. |
| Edizione | [2nd ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (500 pages) |
| Disciplina | 550.2855133 |
| Collana | Springer Textbooks in Earth Sciences, Geography and Environment |
| Soggetto topico |
Geophysics
Geographic information systems Application software Geographical Information System Computer and Information Systems Applications |
| ISBN | 3-031-56906-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Analysis in the Earth Sciences -- Introduction to Python -- Univariate Statistics -- Bivariate Statistics -- Time Series Analysis -- Signal Processing -- Spatial Data -- Image Processing -- Multivariate Statistics -- Directional Data. |
| Record Nr. | UNINA-9910896185203321 |
|
Trauth Martin H.
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Python Recipes for Earth Sciences / / by Martin H. Trauth
| Python Recipes for Earth Sciences / / by Martin H. Trauth |
| Autore | Trauth Martin H. |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (463 pages) |
| Disciplina |
550.028557
550.2855133 |
| Collana | Springer Textbooks in Earth Sciences, Geography and Environment |
| Soggetto topico |
Earth sciences
Geophysics Geographic information systems Earth Sciences Geographical Information System |
| ISBN |
9783031077197
9783031077180 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Analysis in the Earth Sciences -- Introduction to Python -- Univariate Statistics -- Bivariate Statistics -- Time Series Analysis -- Signal Processing -- Spatial Data -- Image Processing -- Multivariate Statistics -- Directional Data. |
| Record Nr. | UNINA-9910616212103321 |
|
Trauth Martin H.
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Signal and Noise in Geosciences : MATLAB® Recipes for Data Acquisition in Earth Sciences / / by Martin H. Trauth
| Signal and Noise in Geosciences : MATLAB® Recipes for Data Acquisition in Earth Sciences / / by Martin H. Trauth |
| Autore | Trauth Martin H. |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (349 pages) |
| Disciplina | 550.2855133 |
| Collana | Springer Textbooks in Earth Sciences, Geography and Environment |
| Soggetto topico |
Geology
Earth sciences Geographic information systems Earth Sciences Geographical Information System |
| ISBN | 3-030-74913-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Acquisition in Earth Sciences -- Introduction to MATLAB -- MATLAB Programming -- Geometric Properties -- Visible Light Images -- Spectral Imaging -- Acquisition of Elastic Signals -- Gravimetric, Magnetic and Weather Data. |
| Record Nr. | UNINA-9910508451503321 |
|
Trauth Martin H.
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||