Advances in Machine Learning and Image Analysis for GeoAI presents recent advances in applications and algorithms that are at the intersection of Geospatial imaging and Artificial Intelligence (GeoAI). The book covers algorithmic advances in geospatial image analysis, sensor fusion across modalities, few-shot open-set recognition, explainable AI for Earth Observations, self-supervised learning, image superresolution, Visual Question Answering, and spectral unmixing, among other topics.
This book offers a comprehensive resource for graduate students, researchers, and practitioners in the area of geospatial image analysis. It provides detailed descriptions of the latest techniques, best practices, and insights essential for implementing deep learning strategies in GeoAI research and applications.

• Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scale
• Chapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among others
• Presents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery