Assessing Individual-Building Vertical Light Exposure in Urban Environments with a Residual Cascade Framework

Artificial Light at Night (ALAN) is increasingly recognized as an environmental exposure in dense urban areas, where conventional two-dimensional nighttime-light remote sensing cannot adequately represent vertical illumination on building facades, while detailed three-dimensional simulations remain computationally expensive for wide-area application. To address this limitation, we developed the Physics-Informed Residual Cascade Framework (PIRCF) for estimating individual-building vertical light exposure from two-dimensional multisource geospatial data. Here, “physics-informed” refers to the incorporation of exposure-related geometric features, distance-related attenuation, spatial-topological relationships, and environmental occlusion priors as inductive biases, rather than the direct enforcement of physical governing equations in the loss function. PIRCF combines graph-based neighborhood inference with residual correction to represent both broad spatial relationships and localized environmental variation. In Guangzhou, the framework achieved R2 values of 0.78 for panchromatic exposure and 0.85 for blue-light exposure, outperforming the selected statistical baselines. In a zero-shot transfer experiment—that is, direct application of the Guangzhou-trained model to Shanghai without additional training or parameter adjustment—the corresponding R2 values were 0.70 and 0.73. The predicted patterns further indicated distinct spectral organizations: panchromatic exposure exhibited broader and more continuous gradients associated with the road network, whereas blue-light exposure showed more fragmented local clustering near commercial and vertically developed urban areas. These findings demonstrate the potential of PIRCF as a scalable screening tool for building-level urban light-exposure assessment and for prioritizing locations requiring more detailed field investigation.