# Zero-shot Building Age Classification from Facade Image Using GPT-4

arXiv: https://arxiv.org/abs/2404.09921 · HTML: https://mlsanity.com/paper/2404.09921/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023)
- [Automatic Prediction of Building Age from Photographs](https://mlsanity.com/paper/1804.02205/) (2018)

## Built on by (most cited first, 8 of 8)

- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?](https://mlsanity.com/paper/2408.10872/) (2024)
- [Automated Building Heritage Assessment Using Street-Level Imagery](https://mlsanity.com/paper/2508.11486/) (2025)
- [Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention](https://mlsanity.com/paper/2512.11811/) (2025)
- [Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models](https://mlsanity.com/paper/2512.21337/) (2025)
- [Table Comprehension in Building Codes using Vision Language Models and Domain-Specific Fine-Tuning](https://mlsanity.com/paper/2511.18306/) (2025)
- [Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications](https://mlsanity.com/paper/2601.06056/) (2025)
- [VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimation](https://mlsanity.com/paper/2603.26015/) (2026)

## Abstract (arXiv)

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.