# Visual Estimation of Building Condition with Patch-level ConvNets

arXiv: https://arxiv.org/abs/1804.10113 · HTML: https://mlsanity.com/paper/1804.10113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN](https://mlsanity.com/paper/2107.10006/) (2021)

## Abstract (arXiv)

The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.