AI-driven optimization of optical imaging and lighting modeling: a framework for landscape visual impact assessment in smart cities

Landscape Visual Impact Assessment (LVIA) plays a critical role in ensuring that urban and landscape developments align aesthetically and ecologically with their surroundings, particularly in the context of smart cities. Traditional LVIA methods often rely on manual inputs and deterministic algorithms, which are inefficient and prone to errors in large-scale or complex environments. This study proposes an AI-driven framework that integrates advanced computational techniques with SketchUp and V-Ray to address these limitations. The framework leverages artificial intelligence (AI) for terrain analysis, vegetation placement, and optical imaging and lighting modeling, significantly enhancing the accuracy, efficiency, and realism of the LVIA process. Key innovations include the use of convolutional neural networks (CNNs) for precise terrain modeling, clustering algorithms for ecologically valid vegetation distribution, and generative adversarial networks (GANs) for photorealistic rendering with optimized lighting effects. The proposed methodology is validated through a coastal landscape case study, demonstrating a 27% reduction in rendering time and a substantial improvement in elevation accuracy compared to traditional methods. The results also highlight that the AI-driven approach achieves a 92% match with real-world vegetation patterns and significantly improves optical lighting accuracy, ensuring visually harmonious and ecologically sustainable outcomes. By automating resource-intensive tasks and enabling scalability, this framework provides a robust solution for modern urban planning projects, advancing LVIA methodologies to support sustainable, adaptive, and visually compelling smart city landscapes.