Wavelet Transform Cluster Analysis of UAV Images for Sustainable Development of Smart Regions Due to Inspecting Transport Infrastructure

Yanyan Zheng, Galina Shcherbakova, Bohdan Rusyn, Anatoliy Sachenko (), Natalya Volkova (), Ihor Kliushnikov and Svetlana Antoshchuk
Additional contact information
Yanyan Zheng: School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
Galina Shcherbakova: Department of Information Systems, Odessa National Polytechnic University, 65044 Odessa, Ukraine
Bohdan Rusyn: Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Radom, 26-600 Radom, Poland
Anatoliy Sachenko: Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Radom, 26-600 Radom, Poland
Natalya Volkova: Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, 65044 Odessa, Ukraine
Ihor Kliushnikov: Department of Computer Systems, Networks and Cybersecurity, National Aerospace University “Kharkiv Aviation Institute”, 61070 Kharkiv, Ukraine
Svetlana Antoshchuk: Department of Information Systems, Odessa National Polytechnic University, 65044 Odessa, Ukraine

Sustainability, 2025, vol. 17, issue 3, 1-27

Abstract: Sustainable development of the Smart Cities and Smart Regions concept is impossible without the development of a modern transport infrastructure, which must be maintained in proper condition. Inspections are required to assess the condition of objects in the transport infrastructure (OTI). Moreover, the efficiency of these inspections can be enhanced with unmanned aerial vehicles (UAVs), whose application areas are continuously expanding. When inspecting OTI (bridges, highways, etc.) the problem of improving the quality of image processing, and analysis of data collected by UAV, for example, is particularly relevant. The application of advanced methods for assessing the quantity of information and making decisions to reduce information uncertainty and redundancy for such systems is often complicated by the presence of noise there. To harmonize the characteristics of certain procedures in such conditions, authors propose conducting data processing using wavelet transform clustering in three main phases: determining the number of clusters, defining the coordinates of cluster centres, and assessing the quality and efficiency of clustering. We compared the efficiency and quality of existing clustering methods with one using wavelet transform. The research has shown that UAVs can be used for OTI inspecting; moreover, the clustering method with wavelet transform is characterised by an improved quality and efficiency of data processing. In addition, the quality assessment enables us to assess the degree of approximation of the clustering result to the ideal one. In addition, authors examined the specific challenges associated with planning UAV flights during inspections to obtain data that will enhance the accuracy of clustering and recognition. This is especially important for a comprehensive quantitative assessment of adaptation degree for image processing procedures to the tasks of inspecting OTI “Smart Cities/Regions” based on a pragmatic measure of informativeness.

Keywords: transportation infrastructure; inspection; UAV; clustering; wavelet transform (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/3/927/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/3/927/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:3:p:927-:d:1574695

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().