Zero-shot Building Age Classification from Facade Image Using GPT-4
Zichao Zeng June Moh Goo Xinglei Wang Bin Chi Meihui Wang Jan Boehm
A building's age of construction is crucial for supporting many geospatial
applications. Much current research focuses on estimating building age from
facade images using deep learning. However, building an accurate deep learning
model requires a considerable amount of labelled training data, and the trained
models often have geographical constraints. Recently, large pre-trained vision
language models (VLMs) such as GPT-4 Vision, which demonstrate significant
generalisation capabilities, have emerged as potential training-free tools for
dealing with specific vision tasks, but their applicability and reliability for
building information remain unexplored. In this study, a zero-shot building age
classifier for facade images is developed using prompts that include logical
instructions. Taking London as a test case, we introduce a new dataset,
FI-London, comprising facade images and building age epochs. Although the
training-free classifier achieved a modest accuracy of 39.69%, the mean
absolute error of 0.85 decades indicates that the model can predict building
age epochs successfully albeit with a small bias. The ensuing discussion
reveals that the classifier struggles to predict the age of very old buildings
and is challenged by fine-grained predictions within 2 decades. Overall, the
classifier utilising GPT-4 Vision is capable of predicting the rough age epoch
of a building from a single facade image without any training.