mlsanityData to 14 Oct 2026

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

arXiv 2208.00881 ↗First posted 1 Aug 2022cs.CVcited by 3

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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.

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

18 of 115 references.

References · 18

  1. 2016Training Region-based Object Detectors with Online Hard Example Miningcited by 579
  2. 2021Where is your place, Visual Place Recognition?cited by 82
  3. 2017Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the UScited by 66
  4. 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
  5. 2017Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networkscited by 64
  6. 2018Building Instance Classification Using Street View Imagescited by 33
  7. 2017Vision-based Real Estate Price Estimationcited by 15
  8. 2018URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer visioncited by 9
  9. 2020Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainabilitycited by 9
  10. 2021Classification of Urban Morphology with Deep Learning: Application on Urban Vitalitycited by 8
  11. 2021Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophotocited by 8
  12. 2018Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligencecited by 7
6 more
  1. 2020Fast and Regularized Reconstruction of Building Faccades from Street-View Images using Binary Integer Programmingcited by 6
  2. 2018Automatic Prediction of Building Age from Photographscited by 5
  3. 2016Rapid building detection using machine learningcited by 4
  4. 2021Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Datacited by 4
  5. 2018Visual Estimation of Building Condition with Patch-level ConvNetscited by 2
  6. 2018Computer Analysis of Architecture Using Automatic Image Understandingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Assessing Building Heat Resilience Using UAV and Street-View Imagery with Coupled Global Context Vision Transformercited by 0
  2. 2026Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagerycited by 0
  3. 2026Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVMcited by 0