  Legibility is the extent to which a space can be easily recognized.
Evaluating legibility is particularly desirable in indoor spaces, since it has
a large impact on human behavior and the efficiency of space utilization.
However, indoor space legibility has only been studied through survey and
trivial simulations and lacks reliable quantitative measurement. We utilized a
Deep Convolutional Neural Network (DCNN), which is structurally similar to a
human perception system, to model legibility in indoor spaces. To implement the
modeling of legibility for any indoor spaces, we designed an end-to-end
processing pipeline from indoor data retrieving to model training to spatial
legibility analysis. Although the model performed very well (98% top-1
accuracy) overall, there are still discrepancies in accuracy among different
spaces, reflecting legibility differences. To prove the validity of the
pipeline, we deployed a survey on Amazon Mechanical Turk, collecting 4,015
samples. The human samples showed a similar behavior pattern and mechanism as
the DCNN models. Further, we used model results to visually explain legibility
in different architectural programs, building age, building style, visual
clusterings of spaces and visual explanations for building age and
architectural functions.
