Congratulations to our ESR 12 from MENELAOSᴺᵀ, Zhouyan Qiu, who successfully defended his PhD thesis on the topic of “Mobile Mapping Applications of Compressive Sensing Techniques ” on February 8, 2024 at the Ingeniería INSITU , Universidade de Vigo under the supervision of Prof. Pedro Arias Sánchez and Dr. Joaquín Martínez-Sánchez.
In the context of earth’s rapid changes, characterized by population growth, accelerated industrialization, and intensified resource exploitation, the Marie Skłodowska-Curie Innovative Training Network Project MENELAOS-NT takes a strategic stance in addressing the imperative of exploring our worldonbothmicroandmacroscales,scientificallyandtechnologically. The initiative seeks to give scientists deeper understanding and offer decision- makers trustworthy data on significant environmental changes, promoting informed and sustainable decision-making.
My academic journey, which began with a bachelor’s degree in Remote Sensing from Wuhan University, Wuhan, China, and further matured with a master’s degree in Geomatics Engineering from the University of Stuttgart, Stuttgart, Germany, naturally aligned with the objectives of MENELAOS-NT. My master’s dissertation, titled ”Geometry-based rail track detection and blocking scenario identification using deep learning”, laid the groundwork for my doctoral pursuits.
Building upon this foundation and situated within the MENELAOS_NT frame-work, my doctoral dissertation explores the domain of Intelligent Infrastructure, leveraging digitalization and Big Data solutions for smart mobility, with a particular emphasis on mobile mapping applications. The dissertation, ac- knowledged with an International Mention and an Industrial Mention, high-lights the synergistic interaction between academia and industry, thereby enhancing the transfer of knowledge from research domains to practical applications and vice versa.
Addressing the imperative need for automatic and real-time data processing to extract pertinent information from transport infrastructure, this dissertation delves into Mobile Mapping Systems (MMS). The focus is on exploring and improving MMS solutions for transportation and infrastructure monitoring applications, emphasizing the development and validation of cost-
effective methods. The research is logically structured around three fundamental pillars, deemed essential for enhancing the effectiveness of MMS: firstly, sensor calibration and evaluation, to ensure accurate and reliable data acquisition; secondly, efficient data compression, to manage the substantial data volumes generated in a resource-effective manner; and thirdly,
practical applications, to validate and demonstrate the utility of the developed MMS in real-world scenarios.
In the initial stages of the dissertation, the focus is placed on reviewing external multi-sensor calibration methodologies and assessing the performance of Time-of-Flight cameras in outdoor settings. This foundational work aims to provide a comprehensive under standing of current calibration techniques and the potential challenges and opportunities associated with using Time-of-Flight cameras in external environments.
Following the data acquisition phase, the amassed data volume is notably extensive. To tackle this challenge, the dissertation introduces an innovative approach specifically tailored for compressing large point cloud datasets. This method aims to achieve high compression ratios while ensuring the preservation of essential data features and details, enhancing the efficient use of MMS data.
In later parts of the dissertation, the focus transitions to the practical applications of low-cost MMS. The research emphasizes creating affordable solutions that maintain accuracy and cater to real-time processing requirements. To provide a comprehensive validation of these proposed solutions, three detailed applications are meticulously explored: traffic sign segmentation, time-to-collisioncalculation, and pavement distressassessment. Each of these applications serves as a profound illustration, highlighting the robust capabilities of low-cost MMS when confronted with authentic challenges in the realms of transportation and infrastructure monitoring.
In conclusion, this doctoral dissertation offers a holistic exploration of MMS, from data acquisition to practical application, aiming to bridge existing gaps and pave the way for the development of reliable, efficient, and cost-effective MMS solutions that can cater to the dynamic needs of contemporary society. A substantial portion of the research was conducted to enhance the services and product offerings of Ingeniería Insitu by integrating multi-sensor solutions for real-world applications. In recognition of its industry-centric contributions, this dissertation has been awarded an Industrial Mention.
This dissertation has been validated through real-world applications, yielding results that address the stated objectives. It comprises six scientific publications: three are published in scientific journals listed in the Journal Citation Report (JCR) Q1, while the other three are peer-reviewed international conference papers.
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