Deep Learning for Multi-Sensor Earth Observation

Discover Deep Learning for Multi-Sensor Earth Observation by Sudipan Saha — an essential guide for anyone working at the intersection of remote sensing, geospatial analysis, and machine learning. This book opens with a compelling overview of why fusing data from optical, SAR, LiDAR and hyperspectral sensors is transforming how we monitor the planet.

Explore practical, state-of-the-art deep learning techniques tailored to real-world geospatial challenges. Saha breaks down convolutional architectures, attention mechanisms, temporal models and transfer learning with clear explanations and hands-on examples focused on multi-sensor fusion, noise-robust feature extraction, and scalable inference for large satellite datasets. Case studies illustrate applications across agriculture, coastal management, urban mapping, disaster response and climate monitoring — demonstrating how fused sensor data improves land-cover classification, change detection and environmental risk assessment.

Imagine faster, more accurate insights from satellite imagery: this book shows you how to turn heterogeneous sensor streams into actionable maps and analytics that inform policy, operations and research. Whether you are a remote sensing scientist, data engineer, GIS professional or advanced student, you’ll gain practical workflows, evaluation strategies and best practices for model generalization across regions and seasons.

Written in a clear, professional voice with geospatial practitioners in mind, Sudipan Saha’s volume balances theoretical rigor with applicative focus — ideal for teams deploying deep learning pipelines for earth observation projects. Ready to advance your geospatial analytics and harness multi-sensor intelligence? Add Deep Learning for Multi-Sensor Earth Observation to your library and start building smarter, more resilient Earth-observation solutions today.

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