# Computer vision-based analysis of buildings and built environments: A systematic review of current approaches

arXiv: https://arxiv.org/abs/2208.00881 · HTML: https://mlsanity.com/paper/2208.00881/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 115 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks](https://mlsanity.com/paper/1711.08681/) (2017)
- [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision](https://mlsanity.com/paper/1809.03609/) (2018)
- [Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability](https://mlsanity.com/paper/2012.14349/) (2020)
- [Classification of Urban Morphology with Deep Learning: Application on Urban Vitality](https://mlsanity.com/paper/2105.09908/) (2021)
- [Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophoto](https://mlsanity.com/paper/2109.03876/) (2021)
- [Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence](https://mlsanity.com/paper/1812.01714/) (2018)
- [Fast and Regularized Reconstruction of Building Faccades from Street-View Images using Binary Integer Programming](https://mlsanity.com/paper/2002.08549/) (2020)
- [Automatic Prediction of Building Age from Photographs](https://mlsanity.com/paper/1804.02205/) (2018)
- [Rapid building detection using machine learning](https://mlsanity.com/paper/1603.04392/) (2016)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Visual Estimation of Building Condition with Patch-level ConvNets](https://mlsanity.com/paper/1804.10113/) (2018)
- [Computer Analysis of Architecture Using Automatic Image Understanding](https://mlsanity.com/paper/1807.04892/) (2018)

## Built on by (most cited first, 3 of 3)

- [Assessing Building Heat Resilience Using UAV and Street-View Imagery with Coupled Global Context Vision Transformer](https://mlsanity.com/paper/2601.11357/) (2026)
- [Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagery](https://mlsanity.com/paper/2604.21102/) (2026)
- [Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM](https://mlsanity.com/paper/2609.17181/) (2026)

## Abstract (arXiv)

Analysing 88 sources published from 2011 to 2021, this paper presents a first systematic review of the computer vision-based analysis of buildings and the built environments to assess its value to architectural and urban design studies. Following a multi-stage selection process, the types of algorithms and data sources used are discussed in respect to architectural applications such as a building classification, detail classification, qualitative environmental analysis, building condition survey, and building value estimation. This reveals current research gaps and trends, and highlights two main categories of research aims. First, to use or optimise computer vision methods for architectural image data, which can then help automate time-consuming, labour-intensive, or complex tasks of visual analysis. Second, to explore the methodological benefits of machine learning approaches to investigate new questions about the built environment by finding patterns and relationships between visual, statistical, and qualitative data, which can overcome limitations of conventional manual analysis. The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.