Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges covers the basic properties, features and models for Earth observation (EO) recorded by very high-resolution (VHR) multispectral, hyperspectral, synthetic aperture radar (SAR), and multi-temporal observations.

• Chapter 1: Introduction
• Part I: Clustering and classification of Earth Observation data
• Chapter 2: Deep learning method for crop classification using remote sensing data
• Chapter 3: Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cover estimation
• Chapter 4: Leveraging twin networks for land use land cover classification
• Chapter 5: Exploiting artificial immune networks for enhancing RS image classification
• Chapter 6: Detection and segmentation of aircrafts in UAV images with a deep learning-based approach
• Part II: Rare event detection using Earth Observation data
• Chapter 7: A transfer learning approach for hurricane damage assessment using satellite imagery
• Chapter 8: Wildfires, volcanoes and climate change monitoring from satellite images using deep neural networks
• Chapter 9: A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study of an Indian state
• Chapter 10: Machine learning paradigm for predicting reservoir property: an exploratory analysis
• Part III: Tools and technologies for Earth Observation data
• Chapter 11: The application of R software in water science
• Chapter 12: Geospatial big data analysis using neural networks
• Chapter 13: Software framework for spatiotemporal data analysis and mining of earth observation data
• Chapter 14: Conclusion