Geohazards

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.