人眼视角环境空间计量与量化方法研究OA

Quantifying the built and natural environment from the human-eye perspective has become a widely used approach in landscape architecture,urban design,environmental psychology,and related fields.With the increasing availability of street-view imagery and advances in computer vision,large-scale human-perspective environmental data can now be routinely extracted and analyzed.However,most existing visual quantification methods rely on two-dimensional image space and represent environmental components by pixel proportions.These methods overlook the fact that human visual perception is based on spherical retinal imaging rather than planar projection,thus creating inconsistencies between computational measurements and actual perceptual experience.Furthermore,pixel-based indicators are sensitive to projection distortions,highly dependent on camera parameters,and incapable of representing depth structures or distinguishing individual landscape elements.As these methods form the basis of numerous data-driven empirical studies,their limitations introduce potential biases and restrict the scientific rigor and application scope of human-perspective environmental research.To address these issues,this study proposes a basic spatial measurement system for human-perspective environmental quantification grounded in angular geometry.Building on theories of retinal projection and spherical visual space,we develop a measurement framework that replaces pixel proportions with angular units that more accurately describe the geometric properties of the visual field.Specifically,the system consists of four components:1)one-dimensional angular measurement using radians to quantify visual position,height,width,and boundary features;2)two-dimensional measurement using solid angles to represent the perceived"area"of visual objects on the spherical retina;3)depth-related representation using relative distance relationships to capture occlusion,spatial layering,and volumetric order;and 4)individual semantic labeling using unique identifiers to distinguish different objects and enable object-level visual impact analysis.These components collectively define a perceptually grounded,geometrically meaningful basis for human-perspective environmental quantification.To evaluate the alignment between different quantification methods and real human perception,the study designed a psychophysical experiment using paired spherical objects.Participants viewed a reference sphere placed at eye level and judged the distance at which a second identical sphere appeared to have half of its visual diameter.Across a range of elevation angles,it recorded the subjective equivalence distances and compared them against measurements obtained from multiple computational methods,including cube mapping,equirectangular projection,fisheye projection,and the proposed angular metrics.Experimental data from 20 participants show that angular metrics—both radian-based and solid-angle-based—produce one-dimensional and two-dimensional size ratios closest to the perceptual benchmark.They outperform all planar projection-based methods in both validity and stability.Analyses further confirm that fisheye projection behaves similarly to angular measurement in many cases,which is consistent with its geometric properties,but still exhibits elevation-dependent variation that angular measurement does not.Based on these theoretical and experimental results,we take the visible green index(GVI)as a representative application to demonstrate the implications of adopting angular metrics in practical landscape analysis.Using a virtual urban plaza model,we computed GVI using the proposed solid-angle method and compared it with GVI derived from conventional equirectangular panoramic imagery.Results show systematic differences:pixel-based GVI consistently underestimates visible greenery,with errors reaching up to 10%in some scenarios.The largest deviations occur in the quantification of trees,whose canopies typically occupy higher elevation angles and thus are more susceptible to projection distortions.Shrubs and grass exhibit relatively smaller deviations.Spatial interpolation of GVI values further reveals that the magnitude and direction of errors vary with viewing distance and vertical distribution of vegetation,reflecting the nonuniform distortions inherent in planar projections.To support efficient and scalable implementation of the proposed method,we developed Env View.P,an open-source tool that integrates the angular measurement framework with a ray-tracing-based spherical sampling workflow.Env View.P converts polygonal mesh models—commonly used in landscape design and simulation—into depth and semantic matrices through dense ray sampling,performs uniform resampling using Fibonacci sphere grids,computes solid angles for each sampling direction,and outputs multi-dimensional environmental indices.The tool is capable of generating high-resolution multi-layer data in seconds,allowing for large-scale analysis,scenario simulation,limited-view analysis,and object-level visual contribution mapping.Demonstration scenarios show its ability to quantify multiple green-view components,evaluate directional visibility,and assess depth-layered vegetation structures,thereby supporting more nuanced design decisions,visibility research,and environmental assessment tasks.Overall,this study establishes a perceptually and geometrically principled framework for human-perspective environmental quantification,validates its accuracy and stability through controlled experiments,and provides an efficient tool for practical application.The findings confirm that angular metrics better represent real visual perception compared with traditional pixel-based approaches,and that adopting such metrics enhances the accuracy and interpretability of GVI and related indicators.The methodology and tool extend the analytical capability of human-perspective landscape studies,supplying reliable geometric foundations and richer data for subsequent research on environmental aesthetics,health-supportive landscapes,embodied perception,environmental equity,and other topics requiring rigorous visual quantification.