Editorial Board Members’ Collection Series: GeoAI in Disaster

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1392

Special Issue Editor


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Guest Editor
Department of Earth and Environment, Boston University, Boston, MA, USA
Interests: spatial analysis and modeling; GIS; data mining and information visualization and artificial neural networks

Special Issue Information

Dear Colleagues,

Communities worldwide are facing more frequent and severe disasters as a result of climate change. To meet this challenge, new tools are needed that can strengthen preparedness, speed recovery, and protect lives and infrastructure. Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative tool, integrating AI with geospatial science to revolutionize disaster prediction, monitoring, response, and recovery. Its ability to process large-scale, multi-source data in real time offers unprecedented potential to save lives and reduce economic losses.

This Special Issue focuses on practical applications of GeoAI across the disaster management cycle, from early warning to long-term recovery We seek contributions that demonstrate novel methodologies, practical implementations, and critical evaluations of GeoAI technologies in addressing global disaster challenges. Topics of interest include, but are not limited to, the following:

  • AI-driven disaster prediction and early warning systems
  • Real-time monitoring using satellite, UAV, and IoT data
  • Post-disaster damage assessment with computer vision and NLP
  • Multi-modal data fusion and large language models in disaster management
  • Ethical considerations, bias mitigation, and scalability of GeoAI solutions

We look forward to receiving your original research articles and reviews.

Prof. Dr. Suchi Gopal
Guest Editor

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Keywords

  • GeoAI
  • disaster management
  • machine learning
  • remote sensing
  • early warning systems
  • risk assessment
  • emergency response
  • climate resilience

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Published Papers (1 paper)

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Research

19 pages, 10467 KB  
Article
Generalizing Human-Driven Wildfire Ignition Models Across Mediterranean Regions Using Harmonized Remote-Sensing and Machine-Learning Data
by Nicola Aimane Dimarco, Ibtissam Faraji, Miriam Wahbi, Mustapha Maatouk, Hakim Boulaassal, Otman Yazidi Aalaoui and Omar El Kharki
Geomatics 2026, 6(1), 13; https://doi.org/10.3390/geomatics6010013 - 1 Feb 2026
Viewed by 729
Abstract
Wildfires represent a growing environmental and socio-economic threat across Mediterranean landscapes, where prolonged summer droughts and human activity increasingly shape ignition susceptibility. This study presents an open and reproducible modelling framework for comparing the relative influence of anthropogenic and biophysical drivers of wildfire [...] Read more.
Wildfires represent a growing environmental and socio-economic threat across Mediterranean landscapes, where prolonged summer droughts and human activity increasingly shape ignition susceptibility. This study presents an open and reproducible modelling framework for comparing the relative influence of anthropogenic and biophysical drivers of wildfire ignition susceptibility across selected Mediterranean regions. Using harmonized 500 m predictors derived from global remote-sensing datasets, we integrate vegetation condition, topography, climatic context, and human pressure indicators within a cloud-based Google Earth Engine workflow. Two tree-based machine-learning models (Random Forest and Extreme Gradient Boosting) are trained and evaluated using spatial cross-validation and cross-region transfer experiments. Results consistently highlight the dominant role of anthropogenic pressure in shaping ignition susceptibility across all study areas, with night-time lights and human modification indices contributing to the largest share of model importance. Both models achieve high predictive performance (AUC > 0.90) and retain stable accuracy under cross-region transfer (mean transfer AUC ≈ 0.85), indicating partial generalization of human-driven ignition patterns across Mediterranean landscapes. Beyond predictive performance, the principal contribution of this work lies in its harmonized cross-regional comparison and explicit evaluation of model transferability using open data and scalable cloud processing. The resulting susceptibility maps provide a transparent and operational basis for comparative wildfire risk assessment and prevention planning within comparable Mediterranean contexts. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: GeoAI in Disaster)
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