Method of Generating Urban Environmental Spatial Layout Based on Multi-Source Data Fusion

The current methods for generating urban environmental spatial layouts face problems such as complex information dimensions and significant differences between visual effects and actual urban layouts, which limit the accuracy and practicality of generating layouts. To overcome these limitations, this paper proposed a three-dimensional urban environment spatial layout generation method that integrated multi-source data and utilizes urban multimodal big language models. This method first obtained basic geographic information such as roads, buildings, vegetation, etc., of the city through OpenStreetMap (OSM), and then used a multimodal big language model of the city to perform semantic understanding and scene analysis on the urban street view images, automatically generating detailed descriptions of the city scene. In the rendering stage, element level rendering techniques were used to independently process and map high-quality textures of each urban element (including building facades, roofs, road textures, etc.) to ensure that the visual effects of each element match the real urban environment. Finally, through an automated asset restructuring process, the rendered elements were accurately placed in the corresponding positions in the city layout based on the location information in OpenStreetMap data, achieving high consistency with the actual city layout. The research results indicate that the urban environmental spatial layout generation method based on multivariate data fusion has achieved excellent performance in layout accuracy with a root mean square error as low as 0.104. In terms of visual authenticity, the user satisfaction ratings for lighting effects and texture matching reached 9.0 and 8.9 points respectively (out of a total of 10 points), and the scalability rating reached 8.4 points. The above data indicates that this technology has reached new heights in accuracy, realism, and computational efficiency, providing an efficient, reliable, and widely applicable tool for urban planning and design.