<div dir="ltr"><p class="MsoNormal" align="center" style="text-align:center;margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>CALL FOR PAPERS</b></p>
<p class="MsoNormal" align="center" style="text-align:center;margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Special Issue on “Advancing
Visual Data Analytics for Disaster Management”<br>
<i>IMAGE AND VISION COMPUTING</i></b></p>
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<p class="MsoNormal" align="center" style="text-align:center;margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><i>Visit the </i><span lang="EL"><a href="https://www.sciencedirect.com/special-issue/322678/advancing-visual-data-analytics-for-disaster-management" target="_blank" style="color:rgb(70,120,134)"><i><span lang="EN-US">Website</span></i></a></span></p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">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 <b>Image and
Vision Computing</b> 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 <b>computer
vision</b>, <b>machine learning</b>, <b>multimodal fusion</b>, <b>privacy-preserving
analytics</b>, <b>explainability</b>, <b>high-performance/edge
computing</b>, and <b>generative simulation</b>. Join us in building a
cross-disciplinary forum where novel algorithms meet operational challenges,
advancing resilience and saving lives through <b>smarter visual data
analytics</b>.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">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.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">This <b>Special Issue</b> on “<b>Advancing Visual
Data Analytics for Disaster Management</b>” 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 <b>disaster
prediction</b>, <b>detection</b>, <b>monitoring</b>, and <b>assessment</b>.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">The Special Issue offers a forum for discussing challenges
in visual data analytics with a primary focus on <b>disaster management</b>.
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.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">We welcome submissions on novel <b>algorithms</b>, <b>methods</b>,
and <b>systems</b> for visual data analytics with direct relevance to
disaster management or similarly <b>critical real-world scenarios</b>.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Topics of interest</b> include, but are not limited
to:</p>
<ul style="margin-top:0cm;margin-bottom:0cm" type="disc">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Advanced <b>deep
learning</b> models for understanding complex visual data in critical
scenarios.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Real-time analytics</b> of visual data
from UAVs, satellites, and social media for disaster response and similar
applications.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Visual data
summarization</b> and
feature extraction for rapid disaster assessment.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Human-centered visual
recognition</b> methods
for disaster scenarios.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Multimodal visual data
analysis</b> integrating
sources like hyperspectral imaging, LIDAR, and thermal imaging.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Generative models</b> for visual data:
simulation of disaster scenarios, in-painting, and handling incomplete
data.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Explainable and
interpretable models</b> to
support decision-making in high-stakes environments.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Privacy-preserving
visual</b> analytics
using methods like differential privacy and federated learning.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Scalable algorithms and
architectures</b> for
large-scale visual data processing in disasters.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>High-performance and
parallel computing</b> approaches
for visual data analytics.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Domain-specific
analytics</b> for
remote sensing, wildfire detection, flood mapping, earthquake damage, etc.</li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Ethical considerations</b> in visual
analytics for disaster management.</li>
</ul>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Submission Guidelines:</b></p>
<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">The Journal's submission system (<span lang="EL"><a href="https://www.editorialmanager.com/imavis/default.aspx" target="_blank" style="color:rgb(70,120,134)"><span lang="EN-US">Editorial Manager</span></a></span>)
is open for submissions. Please refer to the Guide for Authors to prepare your
manuscript and select the article type of “<b>VSI: Visual Data for DM</b>” when
submitting your manuscript online. Both the Guide for Authors and the
submission portal could be found on the Journal Homepage: <span lang="EL"><a href="https://www.elsevier.com/journals/image-and-vision-computing/0262-8856/guide-for-authors" target="_blank" style="color:rgb(70,120,134)"><span lang="EN-US">Guide for
authors - Image and Vision Computing - ISSN 0262-8856 (elsevier.com)</span></a></span>.</p>
<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">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.</p>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Important Dates:</b></p>
<ul style="margin-top:0cm;margin-bottom:0cm" type="disc">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Manuscript Submission
Open Date: <b>July 1st, 2025</b></li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Manuscript Submission
Deadline: <b>October 31st, 2025</b></li>
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Editorial Acceptance
Deadline: <b>February 28th, 2026</b></li>
</ul>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif"><b>Guest Editors:</b></p>
<ol style="margin-top:0cm;margin-bottom:0cm" start="1" type="1">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Prof. Ioannis Pitas
(Department of Informatics, Aristotle University of Thessaloniki, Greece)</li>
</ol>
<ol style="margin-top:0cm;margin-bottom:0cm" start="2" type="1">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Prof. Jose Ramiro
Martinez de Dios (Robotics, Vision and Control Group, University of
Seville, Spain)</li>
</ol>
<ol style="margin-top:0cm;margin-bottom:0cm" start="3" type="1">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Prof. Stefano Berretti
(Media Integration and Communication Center, University of Florence,
Italy)</li>
</ol>
<ol style="margin-top:0cm;margin-bottom:0cm" start="4" type="1">
<li class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">Dr. Ioannis Mademlis
(Department of Informatics, Aristotle University of Thessaloniki, Greece)</li>
</ol>
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<p class="MsoNormal" style="margin:0cm;font-size:12pt;font-family:Aptos,sans-serif">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.</p></div>