Real-time visualization of 3D city models at street-level based on visual saliency

Street-level visualization is an important application of 3D city models.Challenges to street-level visualization include the cluttering of buildings due to fine detail and visualization performance.In this paper,a novel method is proposed for streetlevel visualization based on visual saliency evaluation.The basic idea of the method is to preserve these salient buildings in a scene while removing those that are non-salient.The method can be divided into pre-processing procedures and real-time visualization.The first step in pre-processing is to convert 3D building models at higher Levels of Detail(Lo Ds) into LoD 1 models with simplified ground plans.Then,a number of index viewpoints are created along the streets; these indices refer to both the position and the direction of each street site.A visual saliency value is computed for each building,with respect to the index site,based on a visual difference between the original model and the generalized model.We calculate and evaluate three methods for visual saliency:local difference,global difference and minimum projection area.The real-time visualization process begins by mapping the observer to its closest indices.The street view is then generated based on the building information stored in those indexes.A user study shows that the local visual saliency method performs better than do the global visual saliency,area and image-based methods and that the framework proposed in this paper may improve the performance of 3D visualization.

Evaluating urban development and socio-economic disparity in India through nighttime light data

Balanced development and the reduction of inequality are central objectives of the United Nations Sustainable Development Goals(SDGs).This study explores the use of Nighttime Light(NTL)brightness and the Nighttime Light Development Index(NLDI)as indicators of socioeconomic development in urban centers,focusing on six Indian cities.It examines the correlation between these indices and socioeconomic inequality across affluent neighborhoods,urban slums,downtown areas,and general urban areas in 2015,2018,and 2021.The results reveal that lighting brightness in affluent areas can be lower than that in bustling downtowns,due to factors such as lower residential density.This challenges the conventional assumption that higher NTL necessarily indicates greater prosperity.This study further confirmed significant developmental disparities between well-lit downtowns and poorly illuminated peripheral slum areas,as reflected by lower NLDI scores.Notably,the results uncover a phenomenon termed“same value but different spectrum”based on a careful examination of NLDI values of urban centers and their corresponding curves.This suggests that NLDI alone may not fully capture the complexity of urban development,and that underlying development trajectories,along with on-the-ground realities,must be further examined.The findings emphasize the importance of applying NLDI for urban internal analyses.In addition,the study highlights the necessity for nuanced urban planning and targeted policy interventions specifically tailored to the unique conditions of different urban areas.