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Recent Advances in the Digitalization of Infrastructure

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1505

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Guest Editor
DIING Department of Engineering, University of Palermo, 90100 Palermo, Italy
Interests: 3D survey; 3D modeling; reverse engineering; photogrammetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digitalization of infrastructure is redefining the future of civil engineering, offering unprecedented opportunities to create smarter, more resilient, and more sustainable systems. Advances in remote sensing technologies and building information modeling (BIM) are transforming traditional practices, enabling data-driven decision-making across all phases of infrastructure development and management. This Special Issue seeks to gather pioneering research that pushes the boundaries of how digital tools can revolutionize infrastructure design, construction, monitoring, and maintenance. We particularly welcome studies that explore the integration of remote sensing and BIM in enhancing structural health monitoring, optimizing maintenance strategies, and supporting sustainability objectives. Interdisciplinary approaches that combine engineering, data science, and environmental considerations are especially encouraged. Contributions addressing methodological breakthroughs, innovative case studies, and visions for future smart infrastructure ecosystems are invited. By bringing together diverse perspectives, this Special Issue aims to accelerate the transition toward intelligent, adaptive, and environmentally responsible infrastructure networks, providing a critical platform for academic, industrial, and policy-oriented advancements in the digital era.

Dr. Laura Inzerillo
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • remote sensing
  • BIM
  • civil engineering
  • maintenance design
  • sustainability
  • smart infrastructures
  • smart monitoring

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Published Papers (1 paper)

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Research

24 pages, 5340 KB  
Article
StyleSPADE: Realistic Image Augmentation for Robust Infrastructure Crack Segmentation via Ensemble Learning
by Jaeung Sim, Menas Kafatos, Seung Hee Kim and Yangwon Lee
Appl. Sci. 2026, 16(2), 837; https://doi.org/10.3390/app16020837 - 14 Jan 2026
Cited by 2 | Viewed by 876
Abstract
The rapid deterioration of global infrastructure necessitates precise and automated crack detection technologies for proactive maintenance. However, deep learning-based segmentation models often suffer from a scarcity of diverse, high-quality labeled datasets. This study proposes StyleSPADE, a novel conditional image generation model that integrates [...] Read more.
The rapid deterioration of global infrastructure necessitates precise and automated crack detection technologies for proactive maintenance. However, deep learning-based segmentation models often suffer from a scarcity of diverse, high-quality labeled datasets. This study proposes StyleSPADE, a novel conditional image generation model that integrates semantic masks and style images to synthesize realistic crack data with diverse background textures while preserving precise geometric morphology. To validate the effectiveness of the generated data, we conducted extensive semantic segmentation tasks using Transformer-based (Mask2Former, Swin-UPerNet) and CNN-based (K-Net) models. Experimental results demonstrate that StyleSPADE-based augmentation significantly outperforms baseline models, achieving a Crack IoU of 0.6376 and an F1-score of 0.7586. Furthermore, we implemented a Stacking Ensemble strategy combining high-recall and high-precision models, which further improved performance to a Crack IoU of 0.6452. Our findings confirm that StyleSPADE effectively mitigates the data scarcity problem and enhances the robustness of crack detection in complex environmental conditions. This framework contributes to improving the efficiency and safety of infrastructure management by enabling reliable damage assessment in data-limited environments. Full article
(This article belongs to the Special Issue Recent Advances in the Digitalization of Infrastructure)
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