International Journal of Digital Earth

GeoAI and Big Earth Data for Geohazard Prevention and Resilience

Geohazards such as landslides, debris flows, ground subsidence, slope instability, rockfalls, and cascading hazards pose increasing threats to communities, infrastructure, ecosystems, and sustainable development. Geohazard risk is being reshaped by climate change, rapid urbanization, infrastructure expansion, mining activities, land use change, and increasing disturbance of people and assets in hazardous terrain.

Traditional field investigation and physically based modelling remain essential, but they are often limited by sparse observations, incomplete inventories, complex triggering mechanisms, and strong regional variability. However, the rapid growth of satellite remote sensing, unmanned aerial systems, InSAR, geospatial sensor networks, cloud computing, and multi-source Earth observation has created unprecedented opportunities to observe, model, and understand hazardous Earth surface processes. Furthermore, GeoAI, machine learning, deep learning, and Big Earth Data mining are increasingly transforming geohazards research from simple mapping toward near real-time monitoring, prediction, early-warning, and decision support. Big Earth Data and GeoAI therefore provide new ways to integrate remote sensing, terrain analysis, meteorological data, geological information, deformation measurements, historical inventories, and socio economic exposure data.

While many current data driven models still face challenges related to physical interpretability, uncertainty, generalization, data imbalance, and operational reliability, by promoting studies that connect geospatial artificial intelligence (GeoAI) with geoscientific knowledge and geohazards risk reduction, we have the opportunity to advance more reliable, transparent, and actionable approaches for geohazard prevention and resilience.

This Article Collection aims to bring together advances in GeoAI and Big Earth Data for geohazard prevention and resilience, emphasis on robust approaches, interpretable models, transferable knowledge, and practical applications across diverse geomorphic, climatic, and human disturbed environments.

The Collection welcomes original research articles, review papers, methodological studies, data papers, and application-oriented case studies that fit the scope of the Journal. Relevant topics include, but are not limited to:

• GeoAI and deep learning for geohazards detection and mapping
• Big Earth Data mining for susceptibility, hazard, vulnerability, and risk assessment
• Multi source remote sensing and InSAR for deformation monitoring
• Physically informed and explainable AI for geohazards process understanding
• Transfer learning and foundation models for data scarce regions
• Early warning and decision support systems
• Digital twins and cloud based geohazard platforms
• Multi hazard or cascading risk analysis under climate change and human disturbance

Contributions that demonstrate methodological innovation, open data or reproducible workflows, cross regional transferability, and practical relevance to prevention, mitigation, preparedness, and resilience are particularly encouraged.

Guest Advisor Biography

Gang Mei is a Full Professor of Scientific Computing in Engineering at China University of Geosciences (Beijing). He received his Ph.D. from the University of Freiburg, Germany. His research interests include geohazard prevention and mitigation, computational modeling, machine learning, and big earth data mining. He currently serves on the editorial boards of Bulletin of Engineering Geology and the Environment, Advances in Civil Engineering, PeerJ Computer Science, and Discover Applied Sciences.

Further Information

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling manuscripts.

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 30 April 2027.

Please contact Alex Johnson at [email protected] with any queries and discount codes regarding this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.