Rahul Dev Garg – författare

Apply AI and geospatial analytics to predict and manage geohazards Predicting and responding to floods, landslides, earthquakes, droughts, and wildfires demands more than traditional geospatial methods. Geohazards: AI and Machine Learning in Geospatial Technologies integrates machine learning, deep learning, remote sensing, and GIS into a unified framework for geohazard assessment and disaster response. Edited by a team of geospatial researchers, the book connects data-driven theory with applied disaster resilience strategies. Coverage spans GIS, remote sensing, and GNSS fundamentals through advanced AI-driven risk management, including real-time resource allocation and emergency routing logistics. The book addresses UAV and geospatial applications for data acquisition, search and rescue, and geohazard response. Ethical considerations in deploying AI within geospatial contexts receive dedicated treatment, alongside identification of emerging trends and open research directions. Readers will also find: Real-world case studies demonstrating applied AI and geospatial techniques for specific geohazard scenarios across varied geographic contextsAutomated geospatial analytics workflows that strengthen disaster preparedness through data-driven prediction and continuous environmental monitoring at scaleDeep learning architectures applied to satellite image processing, and LiDAR-based terrain analysis for hazard mappingMethods for integrating synthetic aperture radar and interferometric SAR data into slope stability and subsidence assessmentsFrameworks connecting big data pipelines with geospatial platforms to support policymakers and disaster management agencies in decision-makingDesigned for graduate students, researchers, and professionals in geospatial science, AI, and disaster management, this book provides the technical depth needed to implement machine learning and remote sensing solutions for geohazard prediction. GIS analysts, emergency planners, and policymakers will find actionable frameworks for strengthening disaster resilience.

This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of ‘soil’. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management.