Chapter 1 - Earth observation and GeoAI through the years: six decades of progress in image analysis

Kurzfassung

The goal of this book is to provide a comprehensive introduction to the fundamentals and practical applications of GeoAI for Earth observation (EO) imagery, bridging traditional remote sensing methods with the latest advances in machine learning and high-performance computing. Its four parts cover the full workflow of a typical GeoAI application: from data preprocessing and image enhancement, through advanced analysis techniques, to the computational infrastructures that enable large-scale EO applications. Part I lays the foundation by addressing the challenges inherent in raw EO data. Imagery is often affected by atmospheric effects, such as haze, clouds, and dust, as well as sensor noise, geometric distortions, or transmission artifacts. As a result, preprocessing methods are critical components of the analytical workflow essential to analysis. This part covers fundamental principles that underpin typical EO image preprocessing methods from atmospheric correction (Chapter 2), rectification (Chapter 3), geometric correction (geocoding) (Chapter 4), image registration (Chapter 5), and mosaicking (Chapter 6). Part II introduces techniques for improving image quality. Methods such as pansharpening (Chapter 7), superresolution (Chapter 8), and denoising (Chapter 9) help mitigate common EO data limitations, preparing imagery for downstream GeoAI applications. Part III presents the heart of GeoAI for EO imagery. It introduces fundamental and state-of-the-art approaches to semantic segmentation (Chapter 10), image synthesis (Chapter 11), visualization (Chapter 16), data fusion (Chapter 12), self-supervised learning (Chapter 13), object detection (Chapter 14), and visual question answering (Chapter 15). These topics illustrate how AI is transforming EO analysis, enabling tasks that once required handcrafted algorithms to be performed automatically at scale. Part IV concludes the book by focusing on the computational frameworks that support GeoAI. It discusses geospatial machine learning libraries (Chapter 17), advances in high-performance computing (Chapter 18), and cloud computing solutions (Chapter 19) that allow researchers and practitioners to efficiently process petabyte-scale EO datasets and integrate compute resources closer to where the data reside. This book is intended to serve as a bridge between disciplines and experience levels: •Nonexpert readers and remote sensing specialists will find plain-language explanations of how optical and radar EO imagery is acquired, processed, and analyzed. •Undergraduate and graduate students will see theoretical concepts directly linked to practical examples and exercises. •Industry practitioners will gain insights into building robust, high-performance GeoAI pipelines for real-world EO applications. •Researchers and scientists will encounter discussions of state-of-the-art methods and open challenges to guide future work. This book is designed to serve both as a reference and a learning guide. Each part builds upon the previous one, moving from preprocessing and enhancement of EO imagery to advanced GeoAI analysis methods and the computational infrastructures that enable them. Readers new to the field may wish to begin with the fundamentals in Parts I and II, while those with prior experience can directly explore the analysis techniques in Part III or the computing frameworks in Part IV. Each chapter combines theoretical background with practical considerations and application examples, making it possible to either follow the book sequentially or consult specific sections according to individual interest and expertise.