# Automatic Prediction of Building Age from Photographs

arXiv: https://arxiv.org/abs/1804.02205 · HTML: https://mlsanity.com/paper/1804.02205/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [BuildingNet: Learning to Label 3D Buildings](https://mlsanity.com/paper/2110.04955/) (2021)
- [Zero-shot Building Age Classification from Facade Image Using GPT-4](https://mlsanity.com/paper/2404.09921/) (2024)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models](https://mlsanity.com/paper/2512.21337/) (2025)
- [Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data](https://mlsanity.com/paper/2211.09018/) (2022)

## Abstract (arXiv)

We present a first method for the automated age estimation of buildings from unconstrained photographs. To this end, we propose a two-stage approach that firstly learns characteristic visual patterns for different building epochs at patch-level and then globally aggregates patch-level age estimates over the building. We compile evaluation datasets from different sources and perform an detailed evaluation of our approach, its sensitivity to parameters, and the capabilities of the employed deep networks to learn characteristic visual age-related patterns. Results show that our approach is able to estimate building age at a surprisingly high level that even outperforms human evaluators and thereby sets a new performance baseline. This work represents a first step towards the automated assessment of building parameters for automated price prediction.