# Building Instance Classification Using Street View Images

arXiv: https://arxiv.org/abs/1802.09026 · HTML: https://mlsanity.com/paper/1802.09026/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2018.

## Built on by (most cited first, 30 of 33)

- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [Using Social Media Images for Building Function Classification](https://mlsanity.com/paper/2202.07315/) (2022)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Surveilling Surveillance: Estimating the Prevalence of Surveillance Cameras with Street View Data](https://mlsanity.com/paper/2105.01764/) (2021)
- [Sensing accident-prone features in urban scenes for proactive driving and accident prevention](https://mlsanity.com/paper/2202.12788/) (2022)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [WinSyn: A High Resolution Testbed for Synthetic Data](https://mlsanity.com/paper/2310.08471/) (2023)
- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Urban land-use analysis using proximate sensing imagery: a survey](https://mlsanity.com/paper/2101.04827/) (2021)
- [UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images](https://mlsanity.com/paper/2403.02132/) (2024)
- [Predicting building types and functions at transnational scale](https://mlsanity.com/paper/2409.09692/) (2024)
- [Analyzing Green View Index and Green View Index best path using Google Street View and deep learning](https://mlsanity.com/paper/2104.12627/) (2021)
- [Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images](https://mlsanity.com/paper/2301.00622/) (2023)
- [Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved](https://mlsanity.com/paper/2309.08042/) (2023)
- [Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior](https://mlsanity.com/paper/2401.06550/) (2024)
- [CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI](https://mlsanity.com/paper/2408.05891/) (2024)
- [UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction](https://mlsanity.com/paper/2512.12941/) (2025)
- [Buildings Classification using Very High Resolution Satellite Imagery](https://mlsanity.com/paper/2111.14650/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Visual Perception of Building and Household Vulnerability from Streets](https://mlsanity.com/paper/2205.14460/) (2022)
- [Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery](https://mlsanity.com/paper/2301.00580/) (2023)
- [Automatic Roof Type Classification Through Machine Learning for Regional Wind Risk Assessment](https://mlsanity.com/paper/2305.17315/) (2023)
- [How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery](https://mlsanity.com/paper/2311.17361/) (2023)
- [Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning](https://mlsanity.com/paper/2408.09460/) (2024)

## Abstract (arXiv)

Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US. Keywords: CNN, Building instance classification, Street view images, OpenStreetMap