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 contexts
• Automated geospatial analytics workflows that strengthen disaster preparedness through data-driven prediction and continuous environmental monitoring at scale
• Deep learning architectures applied to satellite image processing, and LiDAR-based terrain analysis for hazard mapping
• Methods for integrating synthetic aperture radar and interferometric SAR data into slope stability and subsidence assessments
• Frameworks connecting big data pipelines with geospatial platforms to support policymakers and disaster management agencies in decision-making

Designed 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.

Kuldeep Chaurasia, PhD, is an Associate Professor at the National Institute of Disaster Management (NIDM), Ministry of Home Affairs, Government of India. Formerly an Associate Professor at Bennett University and a Research Scientist at National Remote Sensing Centre (NRSC), ISRO, his research focuses on artificial intelligence, machine learning, remote sensing and GIS, geospatial analytics, flood mapping, and disaster risk reduction.

Rahul Dev Garg, PhD, is a Distinguished Professor of Geomatics Engineering at the Indian Institute of Technology Roorkee, India. He has led national and international geospatial science projects and authored several books and high impact papers on remote sensing, GIS, and AI-driven geospatial analysis.

Kanaiym Teshebaeva, PhD, is a geoscientist specializing in remote sensing, geospatial analytics, and geohazard assessment. She holds a PhD from the University of Potsdam and GFZ, with expertise in SAR and InSAR technologies for monitoring surface deformation and permafrost dynamics.