Automatic Prediction of Building Age from Photographs
This work addresses the need for automated building parameter assessment, such as for price prediction, by providing a first method for age estimation from photographs, representing an incremental step in computer vision applications.
The authors tackled the problem of automatically estimating building age from photographs by proposing a two-stage deep learning approach that learns visual patterns at patch-level and aggregates them globally, achieving performance that surpasses human evaluators and sets a new baseline.
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.