Explainable numerical representations of otherwise complex datasets are vital
as they extract relevant information, which is more convenient to analyze and
study. These latent representations help identify clusters and outliers and
assess the similarity between data points. The 3-D model of buildings is one
dataset that possesses inherent complexity given the variety in footprint
shape, distinct roof types, walls, height, and volume. Traditionally, comparing
building shapes requires matching their known properties and shape metrics with
each other. However, this requires obtaining a plethora of such properties to
calculate similarity. In contrast, this study utilizes an autoencoder-based
method to compute the shape information in a fixed-size vector form that can be
compared and grouped with the help of distance metrics. This study uses
"FoldingNet," a 3D autoencoder, to generate the latent representation of each
building from the obtained LOD2 GML dataset of German cities and villages. The
Cosine distance is calculated for each latent vector to determine the locations
of similar buildings in the city. Further, a set of geospatial tools is
utilized to iteratively find the geographical clusters of buildings with
similar forms. The state of Brandenburg in Germany is taken as an example to
test the methodology. The study introduces a novel approach to finding similar
buildings and their geographical location, which can define the neighborhood's
character, history, and social setting. Further, the process can be scaled to
include multiple settlements where more regional insights can be made.Comment: 10 pages, 6 figure