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Supervised Learning in Remote Sensing and Geospatial Science
◆日本地質学会第132年学術大会セール 開催中！：2025年10月24日(金)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Title: Supervised Learning in Remote Sensing and Geospatial Science Author: E Maxwell, Aaron (Assistant Professor, Department of Geology and Geography, West Virginia University, West Virginia, USA, Director, West Virginia View, USA, and Faculty Director, West Virginia GIS Technical Center, USA) / Ramezan, Christopher (Assistant P Publisher: Elsevier ISBN: 9780443293061 Cover: PAPERBACK Date: 2025年10月 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. TABLE OF CONTENTS Part I: Supervised Learning and Key Principles 1. Introduction to the Supervised Learning Proces What is supervised learning? What is artificial intelligence and machine learning? Uses of supervised learning in remote sensing and geospatial science Supervised vs. unsupervised learning Overview of the supervised learning process Data requirements Bias and variance Model performance and assessment Overfitting and generalization Toward reproducibility and replicability Introduction to provided R and Python examples 2. Training Data and Labels Training data requirements and considerations Collection and labelling methods Sampling Quality assurance and quality control Optimizing reproducibility and replicability 3. Accuracy Assessment Validation data characteristics and considerations Assessment metrics Estimating the uncertainty of predictions Comparing model performance 4. Predictor Variables and Data Considerations Remotely sensed data sources Ancillary geospatial data Complexities of working with multiple data sources Feature space engineering Data pre-processing Part II: Supervised Learning Algorithms 5. Supervised Learning with Linear Methods Linear regression Multiple linear regression Generalized linear models (GLMs) Regression for binary classification Generalized additive models (GAMs) 6. Machine Learning Algorithms k-nearest neighbour (kNN) algorithm Tree-based methods Kernel-based methods Artificial neural networks Ensemble methods 7. Tuning Hyperparameter and Improving Models Hyperparameter tuning Improving models 8. Geographic Object-Based Image Analysis (GEOBIA) Why use object-based methods? Image segmentation algorithms Multiscale image segmentation Feature summarization Classifying objects and selecting training and validation objects Post-processing Accuracy assessment considerations Part III: Supervised Learning with Deep Learning 9. Deep Learning for Scene-Level Problems Review of artificial neural networks (ANNs) Tensor data model Computational requirements Loss metrics Optimization Activation functions Training process Fully connected neural networks Convolutional neural networks (CNNs) 10. Deep Learning for Pixel-Level Problems Upsampling and transpose convolution Fully convolutional neural networks (FCNNs) Encoder-decoder architectures Spatial pyramid pooling and multiscale architectures Encoder backbones PointRend Instance segmentation Accuracy Assessment considerations 11. Improving Deep Learning Models Preparing input data Choosing an architecture Dealing with small sample sizes Class imbalance Architectural manipulations Augmenting the learning process Semi-supervised learning 12. Frontiers and Supervised Learning at Scale Vision transformers Learning from unlabelled data High performance computing
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