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Full Description
The role of artificial intelligence is crucial in the domain of Earth Observation (EO) data analysis. Deep learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex deep learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems.
Features
Examines explainability of algorithms from the aspect of generalizability and reliability.
Reviews state-of-the-art explainability strategies related to the preprocessing algorithms.
Provides explanations for specific evaluation metrics of various EO data processing and preprocessing algorithms.
Discusses explainable ante-hoc and post-hoc approaches for EO data analysis.
Serves as a foundational reference for developing future EO data processing strategies.
Address the key challenges in making EO data processing algorithms interpretable and offers insights for the future of explainable EO data processing.
This book is intended for graduate students, researchers and academics in computer and data science, machine learning, and image processing, as well as professionals in geospatial data science using GIS and remote sensing in Earth and environmental sciences.
Contents
1. Towards Explainable Geospatial AI. 2. Explainable AI Methods: Challenges and Opportunities for EO Data Analysis. 3. Explainable EO Data Pre-processing: Challenges and Way Forward. 4. Explainable Feature Engineering for EO Data Analysis. 5. Towards Explainable Discriminative Models for EO Data Analysis. 6. Towards Explainable Generative Models for EO Data Analysis. 7. Earth Observation Data Analytics: Explainable AI (XAI) Strategies. 8. Towards Correlating Deep Learning Models with Physics-based Models. 9. Explainable Ante-hoc Approaches for EO Data Analysis: Opportunities and Challenges. 10. Explainable Post-hoc Approaches for EO Data Analysis: Opportunities and Challenges. 11. Online Learning Strategies for Explainability. 12. Explainability based Evaluation Metrics. 13. Benchmark Datasets for EO Data Explainability. 14. Applications and Case Studies of Explainable EO Data Analysis. 15. Future Trends in Explainable AI for Geospatial Applications.
