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Special Issue on “Advancing Visual Data Analytics for Disaster Management”
IMAGE AND VISION COMPUTING

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From torrents of satellite imagery to drone video streams and citizen-generated footage, visual data now shapes how we forecast, respond to, and recover from catastrophes. This Special Issue of Image and Vision Computing journal seeks state-of-the-art research that converts these heterogeneous visual streams into trustworthy, real-time intelligence for natural- and human-made disaster management. We welcome breakthroughs in computer vision, machine learning, multimodal fusion, privacy-preserving analytics, explainability, high-performance/edge computing, and generative simulation. Join us in building a cross-disciplinary forum where novel algorithms meet operational challenges, advancing resilience and saving lives through smarter visual data analytics.

With the increasing frequency and severity of natural and man-made disasters, effective disaster management is a global priority. Visual data from any source play a vital role in disaster preparedness, response, and recovery. Efficient and accurate analysis of this visual data is crucial for understanding disaster scale and impact, while having significant implications for broader challenges in visual data analytics.

This Special Issue on “Advancing Visual Data Analytics for Disaster Management” seeks to present cutting-edge methodologies, emerging applications, and core challenges in deriving actionable insights from visual data in disaster contexts. Emphasis is placed on advanced computer vision, machine learning, and data science methods for processing visual data streams in real-time or near-real-time, supporting disaster prediction, detection, monitoring, and assessment.

The Special Issue offers a forum for discussing challenges in visual data analytics with a primary focus on disaster management. Potential applications include, not exhaustively, flood monitoring, wildfire tracking, earthquake damage assessment, and urban disaster response. The aim is to foster collaboration across disciplines – computer vision, machine learning, data science – and identify future research directions.

We welcome submissions on novel algorithms, methods, and systems for visual data analytics with direct relevance to disaster management or similarly critical real-world scenarios.

Topics of interest include, but are not limited to:

• Advanced deep learning models for understanding complex visual data in critical scenarios.
• Real-time analytics of visual data from UAVs, satellites, and social media for disaster response and similar applications.
• Visual data summarization and feature extraction for rapid disaster assessment.
• Human-centered visual recognition methods for disaster scenarios.
• Multimodal visual data analysis integrating sources like hyperspectral imaging, LIDAR, and thermal imaging.
• Generative models for visual data: simulation of disaster scenarios, in-painting, and handling incomplete data.
• Explainable and interpretable models to support decision-making in high-stakes environments.
• Privacy-preserving visual analytics using methods like differential privacy and federated learning.
• Scalable algorithms and architectures for large-scale visual data processing in disasters.
• High-performance and parallel computing approaches for visual data analytics.
• Domain-specific analytics for remote sensing, wildfire detection, flood mapping, earthquake damage, etc.
• Ethical considerations in visual analytics for disaster management.

Submission Guidelines:

The Journal's submission system (Editorial Manager) is open for submissions. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: Visual Data for DM” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Guide for authors – Image and Vision Computing – ISSN 0262-8856 (elsevier.com).

Submissions must follow the IMAGE AND VISION COMPUTING journal’s formatting and submission requirements. All manuscripts will undergo rigorous peer review. Contributions must be original and unpublished, focusing on visual data analytics methods and their applications in disaster management.

Important Dates:

• Manuscript Submission Open Date: July 1st, 2025
• Manuscript Submission Deadline: October 31st, 2025
• Editorial Acceptance Deadline: February 28th, 2026

Guest Editors:

1. Prof. Ioannis Pitas (Department of Informatics, Aristotle University of Thessaloniki, Greece)

2. Prof. Jose Ramiro Martinez de Dios (Robotics, Vision and Control Group, University of Seville, Spain)

3. Prof. Stefano Berretti (Media Integration and Communication Center, University of Florence, Italy)

4. Dr. Ioannis Mademlis (Department of Informatics, Aristotle University of Thessaloniki, Greece)

We look forward to your contributions to this Special Issue on advancing visual data analytics for more effective disaster management and similar real-world applications.