Supervised Learning in Remote Sensing and Geospatial Science is a practical reference on supervised learning and associated best practices for applications in remote sensing and geospatial data science, in the context of practical and applied mapping and modeling tasks. With an emphasis on practicality, the book covers all supervised learning processes associated with developing labeled datasets to train and evaluate models, along with methods for combating common problems such as data imbalance, and direction on assessing model performance. Methods for preparing a wide variety of remotely sensed and geospatial data as input to supervised learning workflows are discussed.

With a focus on bridging the gap between theory and practice, Supervised Machine Learning in Remote Sensing and Geospatial Data equips researchers, practitioners, and students with the necessary tools and techniques to extract actionable information from raw geospatial data.

• Provides a practical handbook for implementing supervised machine learning techniques to geospatial data, with step-by-step methodology and case studies
• Discusses the full spectrum of machine and deep learning methods for classification and regression tasks
• Focuses on applied implementation, common issues, pitfalls, and best practices; providing practical considerations on dealing with these problems