Advances in Machine Learning and Image Analysis for GeoAI 1st Edition

Advances in Machine Learning and Image Analysis for GeoAI — 1st Edition, edited by Saurabh Prasad; Jocelyn Chanussot; Jun Li

Harness the next wave of geospatial intelligence with this authoritative volume that bridges cutting-edge machine learning and practical image analysis for GeoAI applications. Ideal for researchers, GIS professionals, remote sensing engineers, and advanced students, this book delivers rigorous methods and real-world examples that address land cover mapping, urban monitoring, disaster response, environmental change detection, and climate impact studies.

Inside you’ll find clear explanations of deep learning architectures, domain adaptation, transfer learning, and explainable AI tailored for satellite imagery and multispectral data. Case studies demonstrate how to turn high-resolution remote sensing and sensor-fusion outputs into actionable insights for policy makers and field teams. Practical algorithmic strategies are paired with discussions on scalability, uncertainty quantification, and ethical use of spatial data.

This edition stands out for its balance of theory and application: concise mathematical foundations, reproducible workflows, and forward-looking perspectives on GeoAI’s role in sustainability and smart cities. Whether you’re building end-to-end image-analysis pipelines or integrating ML models into GIS, the book equips you with the tools to extract reliable, geospatially-aware intelligence from complex datasets.

Advance your expertise and stay competitive in the rapidly evolving GeoAI landscape. Order your copy today and bring state-of-the-art machine learning and image analysis into your geospatial toolkit.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.