Automatic Prediction of Building Age from Photographs

TL;DR

This paper introduces a novel two-stage deep learning method for automatically estimating building age from photographs, outperforming human evaluators and enabling automated property assessment.

Contribution

The work is the first to automate building age prediction from images using a patch-based deep learning approach with comprehensive evaluation.

Findings

Achieves high accuracy in building age estimation

Outperforms human evaluators in accuracy

Sets a new performance baseline for automated building assessment

Abstract

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.