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Full Description
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.
Contents
Part I: Supervised Learning and Key Principles
1. Introduction to the Supervised Learning Proces
2. Training Data and Labels
3. Accuracy Assessment
4. Predictor Variables and Data Considerations
Part II: Supervised Learning Algorithms
5. Supervised Learning with Linear Methods
6. Machine Learning Algorithms
7. Tuning Hyperparameter and Improving Models
8. Geographic Object-Based Image Analysis (GEOBIA)
Part III: Supervised Learning with Deep Learning
9. Deep Learning for Scene-Level Problems
10. Deep Learning for Pixel-Level Problems
11. Improving Deep Learning Models
12. Frontiers and Supervised Learning at Scale
