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Presently, immense attention is on sustainable developments because of the energy crisis, which has inundated the world at an alarming level. As a result, sustainability has become a major consideration that must be given priority while planning and developing modern urban areas. This study examined thermal signatures from selected colours of housing envelopes (roofing materials) used for construction in most rural and urban areas in Nigeria. Data for the study comprised thermal readings from 5 different colours of these materials collected over morning, afternoon and evening periods. Analysis of Variance, ANOVA, was used to test whether the variation across various colours and temporal periods was significant. Findings show that green roofing material has an average reading of 39.80C, while black roofing material has an average of 55.60C. Besides, orange-coloured material has an average of 34.10C, while beige colour has an average of 34.70C. Silver colour has an average of 32.10C. From the result of the analysis, it was discovered that there exists a significant variation in the thermal absorptive characteristics of the roofing materials considered. Temporally, high temperature >400C was observed to have been emitted from black and green colours all through afternoon and evening periods suggesting that these colours have high thermal absorptivity. It was therefore recommended that urban development authorities, architects and homeowners should always adopt the right and efficient colours of roofing materials to reduce high incidences of heat absorption and transfer from these materials and also conserve energy that would have been used for cooling or warming. Colours with high reflectivity should be used if a maximum cooling effect is desired. Black and green colours should be avoided in tropical regions due to low solar reflectivity, high thermal absorptivity and heat flux. Building owners should avoid substandard and untreated building materials (non-cool roofs) during roofing construction for good ambience.

This review explores the role of artificial intelligence in enhancing risk management for road and rail transport systems, with a particular focus on the Eastern European context. In an era marked by increasing exposure to natural hazards and aging infrastructure, artificial intelligence technologies offer innovative tools for predictive maintenance, object detection, and real-time hazard monitoring. Drawing on five international case studies, the analysis synthesizes key developments and identifies potential applications relevant to the Eastern European region, demonstrating how technological advancements can serve as models and inspiration. Emphasis is placed on integrating artificial intelligence with Geographic Information Systems and remote sensing, highlighting how these technologies can support early warning systems and spatial decision-making. The paper discusses current limitations in the region, including gaps in funding, data infrastructure, and institutional readiness, and proposes directions for future research and regional policy strategies. The analysis underscores the necessity of adopting intelligent systems to enhance the resilience and sustainability of transport in hazard-prone areas.