UAVs and GeoAI for Natural Hazard Monitoring and Modeling

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have very quickly evolved into essential tools for observing, mapping, and analyzing natural hazards, thanks to their flexibility, rapid deployment, and ability to acquire high-resolution data in complex and dynamic environments. Recent advances in onboard sensors, positioning systems, and data acquisition strategies have significantly expanded the range of UAV applications across the disaster risk management cycle, including preparedness, response, recovery, and mitigation. At the same time, the increasing availability of large, high-quality UAV datasets has created new opportunities for data-driven analysis and automated interpretation.

In this context, Geospatial Artificial Intelligence (GeoAI) has emerged as a powerful framework for extracting knowledge from UAV-derived data by integrating machine learning, deep learning, and spatial analytics. The combination of UAV technologies with GeoAI enables more accurate, scalable, and timely monitoring and modeling of natural hazards such as landslides, floods, wildfires, volcanic activity, and coastal erosion. These integrated approaches support improved understanding of hazard dynamics, enhance early-warning capabilities, and contribute to more informed decision-making for risk reduction and emergency management.

The goal of this Special Issue is to bring together papers (original research articles and review papers) that provide insights about the integration of UAV technologies and GeoAI methods for monitoring, mapping, and modeling natural hazards and highlight methodological advances, operational applications, and emerging challenges. This topic is fully aligned with the scope of Drones, as it emphasizes UAV platforms, sensors, data processing workflows, and innovative applications in environmental monitoring and disaster-related scenarios.

We welcome manuscripts that are related to the following themes:

• UAV-based data acquisition, photogrammetry, and remote sensing for natural hazards;
• GeoAI, machine learning, and deep learning methods applied to UAV-derived geospatial data;
• Integrated UAV–GeoAI frameworks for hazard monitoring, modeling, early warning, and emergency response.

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

Dr. Vasil Yordanov
Dr. Aggeliki Kyriou
Dr. Polychronis Kolokoussis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• UAVs
• geoAI
• natural hazards
• remote sensing
• hazard monitoring and mapping
• geospatial data analytics