Advancing Visual Data Analytics for Disaster Management Call for Papers

CALL FOR PAPERS

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

 

https://www.sciencedirect.com/special-issue/322678/advancing-visual-data-analytics-for-disaster-management 

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) will be open for
submissions to our Special Issue from July 1st, 2025. 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:

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

e-mail address: pitas@csd.auth.gr

WWW page: https://aiia.csd.auth.gr/computer-vision-machine-learning/#people

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

e-mail address: jdedios@us.es

WWW page: https://www.us.es/trabaja-en-la-us/directorio/jose-ramiro-martinez-de-dios

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

e-mail address: stefano.berretti@unifi.it

WWW page: http://www.micc.unifi.it/berretti/

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

e-mail address: imademlis@csd.auth.gr

 

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.