## 1 Introduction
They discuss the prevalence of proprietary digital twin software solutions and introduce an open-source software framework for digital twins, known as the Digital Twin Operational Platform (DTOP). This framework, implemented through a browser-based platform using Python and Flask, aims to enhance connectivity between users and data from the physical twin, including Internet of Things (IoT) devices. Python is chosen for its versatility in web-based and scientific applications, and Flask serves as a crucial server tool, enabling remote hosting and access. The authors emphasize the vast potential for future research in open-source digital twin software with network connectivity. The web interface, designed using HTML5, CSS, and JavaScript, ensures a responsive, interactive, and animated browser-based graphical user interface. Flask, along with Python, enables seamless connectivity among various components, empowering users to interact with the digital twin through a web browser without specific programming knowledge. Back-end users can download the open-source project, making customized modifications to align with project-specific requirements. The authors of Ref. [12] conducted a scoping review of 162 papers, spanning from 2014 to 2021, across various urban fields to address gaps in urban sustainability and spatial data analysis. The overview reveals insights into studies utilizing artificial intelligence and machine learning with geospatial data for analyzing urban areas. Concentrated cases are found in China, the US, and the UK, with regional breakdowns indicating significant studies in Europe, Asia, and North America. Focused on leveraging geospatial data for urban sustainability, the study emphasizes effective algorithms like random forests and support vector machines. The study highlights the importance of comparing different machine learning algorithms within specific topics and calls for new methodological frameworks that go beyond the application of machine learning to explain, translate, and transfer results to urban sciences, practice, and decision policies. While machine learning and artificial intelligence gain momentum, the authors advocate for standardizing the selection of data, algorithms, and parameters to provide concrete reasoning for sustainable urban planning decisions and policies. The review also acknowledges limitations in developing spatial machine learning studies in data-sparse areas, particularly in the Global South. In connection to this, the main goal of Ref. [13] is to develop a data-driven prediction model for water main failures in the City of Kitchener, Ontario, Canada, with the goal of reducing field inspections through proactive interventions. By limiting field inspections to pipes more likely to fail in the future, the study contributes to preventive maintenance practices and cost-effective updates of asset management plans. The results indicate that over 72% of issues could potentially have been prevented by monitoring and upgrading only 8% of the network. Random forest models outperformed other algorithms in terms of accuracy. The study emphasizes the use of city condition indicators for better predictability and insights into root causes, enhancing the reliability and resilience of city infrastructure. In a comprehensive analysis of 312 abstracts, along with a detailed examination of 72 full papers, Ref. [14] explores the intersection of digital twins and smart cities in the context of disaster risk management. The review assesses the evolution of digital twins, explores smart technologies used in disaster risk management, and assesses the state-of-the-art and technical feasibility of smart city-based approaches in this area. The article highlights the need for enhanced techniques to capture the spatial aspects of disruptions to the built environment. The authors suggest the need to develop a real-time resilience mapping system using geoparsing techniques to extract named entities at the street level.