Shape Pattern Recognition of Building Footprints Using t-SNE Dimensionality Reduction Visualization

Abstract

The shape pattern recognition of building footprints stands as a pivotal concern within GIS spatial cognition. In this study, we introduce a novel approach for the shape recognition of building footprints, leveraging t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction visualization. First, the Canonical Time Warping (CTW) algorithm is employed to gauge the shape similarity distance of building footprints. Subsequently, the t-SNE model is utilized to map the building footprints, featuring varying numbers of coordinate vertices, onto points within the Cartesian coordinate system. The shape similarity distance serves as the input to the t-SNE model for parameter optimization. Lastly, building footprint shapes are identified through the inherent clustering patterns of points using a Gaussian Mixture Model (GMM). Experimental results demonstrate the method’s robustness to the translation, rotation, scaling, and mirroring of geometric objects, while effectively measuring shape similarity between building footprints. Furthermore, diverse types of building footprints are discernible through natural clustering in low-dimensional spaces, aligning closely with human visual perception.