| Titre : | Machine learning on geographical data using Python : introduction into geodata with applications and use cases |
| Auteurs : | Joos Korstanje, Auteur |
| Type de document : | texte imprimé |
| Editeur : | New York : Apress, 2023 |
| ISBN/ISSN/EAN : | 978-1-4842-8286-1 |
| Format : | XV, 312 p. / ill. / 25 cm |
| Note générale : | Index |
| Langues : | Anglais |
| Index. décimale : | 004.89 (Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente.) |
| Tags : | Geodatabases Machine learning Python (Computer program language) |
| Résumé : |
Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python.
This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. |
| Note de contenu : |
Summary :
Part I: General introduction Chapter 1: Introduction to Geodata Chapter 2: Coordinate Systems and Projections Chapter 3: Geodata Data Types Chapter 4: Creating Maps Part II: GIS operations Chapter 5: Clipping and Intersecting Chapter 6: Buffering Chapter 7: Merge and Dissolve Chapter 8: Erase Part III: Machine Learning and mathematics Chapter 9: Interpolation Chapter 10: Classification Chapter 11: Regression Chapter 12: Clustering Chapter 13: Conclusion |
Exemplaires (2)
| Cote | Support | Localisation | Section | Disponibilité | Etat_Exemplaire |
|---|---|---|---|---|---|
| 004.89 KOR | Papier | Bibliothèque Centrale | Data sciences_Intelligence artificielle | Disponible | Consultation sur place |
| 004.89 KOR | Papier | Bibliothèque Centrale | Data sciences_Intelligence artificielle | Disponible | Consultation sur place |
