Abstract Current landscape visibility data lacks systematic organization and deep mining. This study proposes a multiscale landscape–ground position visibility model, constructing a 4D tensor integrating visibility relationships across multiple viewing radii. Mining patterns like Minimum Coverage Equivalent Cell Classes (MCECC) and Minimum Coverage Visibility Convex Polygons (MCVCP) are designed to calculate landscape visual protection zones based on minimal observation positions. Using the Gubeikou Great Wall as a case study, a multiscale visibility dataset was constructed. Results show that for the Jinshanling section, MCVCP reduces the required protection area by 90% and 70% compared to the buffer zone and Reverse Zones of Theoretical Visibility (R-ZTV) methods, respectively. For Gubeikou, the reductions are 73% and 49%. Furthermore, complete visual coverage is achieved with a maximum of only 4 and 12 observation points for the two sites, respectively.
Chen et al. (Tue,) studied this question.