Estimating seasonal urban greenery with street view and satellite imagery: a cross-season weight regression approach

However, the resource-intensive nature of SVI acquisition leads to substantially lower temporal resolution and irregular sampling intervals. This results in significant temporal gaps in historical SVI, rendering them unsuitable for extracting and analyzing seasonal GVI variations.

This may potentially introduce biases into related research. In this study, the seasonal GVI gaps are estimated using the cross-season weight regression (CSWR) model, which significantly improves the accuracy of GVI estimation for missing seasons through an innovative modification of the classical local regression (LR) model. For intra-season and cross-season auxiliary data points, CSWR constructs local and seasonal weights using Gaussian kernel functions and Pearson distance metrics, respectively.