Supervised Learning in Remote Sensing and Geospatial Science is an invaluable resource focusing on practical applications of supervised learning in remote sensing and geospatial data science. Emphasizing practicality, the book delves into creating labeled datasets for training and evaluating models. It addresses common challenges like data imbalance and offers methods for assessing model performance. This guide bridges the gap between theory and practice, providing tools and techniques for extracting actionable information from raw geospatial data.
The book covers all aspects of supervised learning workflows, including preparing diverse remotely sensed and geospatial data inputs. It equips researchers, practitioners, and students with essential knowledge for applied mapping and modeling tasks, making it an indispensable reference for advancing geospatial science.
1. Chapter 1. Introduction to the supervised learning process - p. 1 2. Chapter 2. Training data and labels - p. 19 3. Chapter 3. Predictor variable considerations - p. 35 4. Chapter 4. Data preparation and preprocessing - p. 69 5. Chapter 5. Model assessment methods and metrics - p. 103 6. Chapter 6. Supervised learning with linear and additive methods - p. 137 7. Chapter 7. Machine learning algorithms - p. 161 8. Chapter 8. Tuning hyperparameters and improving models - p. 191 9. Chapter 9. Geographic object-based image analysis - p. 215 10. Chapter 10. Fully connected neural networks for scene-level predictions - p. 243 11. Chapter 11. Convolutional neural networks for scene-level predictions - p. 269 12. Chapter 12. Deep learning for pixel-level predictions - p. 293 13. Chapter 13. Deep learning for object detection and instance segmentation - p. 317 14. Chapter 14. Improving deep learning models - p. 335 15. Chapter 15. Current trends and frontiers - p. 365 16. Index - p. 401
Supervised Learning in Remote Sensing and Geospatial Science is an invaluable resource focusing on practical applications of supervised learning in remote sensing and geospatial data science. Emphasizing practicality, the book delves into creating labeled datasets for training and evaluating models. It addresses common challenges like data imbalance and offers methods for assessing model performance. This guide bridges the gap between theory and practice, providing tools and techniques for extracting actionable information from raw geospatial data.
The book covers all aspects of supervised learning workflows, including preparing diverse remotely sensed and geospatial data inputs. It equips researchers, practitioners, and students with essential knowledge for applied mapping and modeling tasks, making it an indispensable reference for advancing geospatial science.
1. Chapter 1. Introduction to the supervised learning process - p. 1 2. Chapter 2. Training data and labels - p. 19 3. Chapter 3. Predictor variable considerations - p. 35 4. Chapter 4. Data preparation and preprocessing - p. 69 5. Chapter 5. Model assessment methods and metrics - p. 103 6. Chapter 6. Supervised learning with linear and additive methods - p. 137 7. Chapter 7. Machine learning algorithms - p. 161 8. Chapter 8. Tuning hyperparameters and improving models - p. 191 9. Chapter 9. Geographic object-based image analysis - p. 215 10. Chapter 10. Fully connected neural networks for scene-level predictions - p. 243 11. Chapter 11. Convolutional neural networks for scene-level predictions - p. 269 12. Chapter 12. Deep learning for pixel-level predictions - p. 293 13. Chapter 13. Deep learning for object detection and instance segmentation - p. 317 14. Chapter 14. Improving deep learning models - p. 335 15. Chapter 15. Current trends and frontiers - p. 365 16. Index - p. 401
