The detection of objects has become a critical step to update ground cover information, and the availability of very high-resolution satellite images made us discover new classification methods that give us more details such as pixel classification. This study aims to explore the potential and performance of machine learning algorithms in poor urban conditions in order to show the power of the deep neural networks to detect objects and, more precisely, to detect roads. We propose a U-net architecture for road extraction from Massachusetts dataset. The results have been compared with different automatic classification learning algorithms. The results of the classification using U-net showed a high accuracy of 97.7%, more precise than all the other models, which is why it is the best method to solve classification tasks for objects detection in large-scale datasets.

Digital Twin (DT) technology has emerged as a transformative tool in urban flood risk management (UFRM), enabling real-time data integration, predictive modeling, and decision support. This systematic review synthesizes existing literature to evaluate the scientific impact, technological advancements, and practical applications of DTs in UFRM. Using the PRISMA 2020 framework, we retrieved 1085 records (Scopus = 85; Web of Science = 1000), merged and deduplicated them using DOI and fuzzy-matched titles, screened titles/abstracts, and assessed full texts. This process yielded 85 unique peer-reviewed studies published between 2018 and 2025. Key findings highlight the role of remote sensing (e.g., satellite imagery, IoT sensors) in enhancing DT accuracy, the integration of machine learning for predictive analytics, and case studies demonstrating reduced flood response times by up to 40%. Challenges such as data interoperability and computational demands are discussed, alongside future directions for scalable, AI-driven DT frameworks. This review identifies key technical and governance challenges while recommending the development of modular, AI-driven DT frameworks, particularly tailored for resource-constrained regions.