# Classification of Urban Morphology with Deep Learning: Application on Urban Vitality

arXiv: https://arxiv.org/abs/2105.09908 · HTML: https://mlsanity.com/paper/2105.09908/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 89 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [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)
- [A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood](https://mlsanity.com/paper/1705.02198/) (2017)
- [Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet](https://mlsanity.com/paper/1709.02939/) (2017)

## Built on by (most cited first, 8 of 8)

- [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [GANmapper: geographical data translation](https://mlsanity.com/paper/2108.04232/) (2021)
- [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://mlsanity.com/paper/2208.00881/) (2022)
- [From Geometric Mimicry to Comprehensive Generation: A Context-Informed Multimodal Diffusion Model for Urban Morphology Synthesis](https://mlsanity.com/paper/2409.17049/) (2024)
- [FastFlow: AI for Fast Urban Wind Velocity Prediction](https://mlsanity.com/paper/2211.12035/) (2022)
- [Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates](https://mlsanity.com/paper/2412.12183/) (2024)
- [Decoding street network morphologies and their correlation to travel mode choice](https://mlsanity.com/paper/2507.19648/) (2025)

## Abstract (arXiv)

There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from the human's visual and intuitive perspective. We take the first step to bridge the gap by proposing a deep learning-based technique to automatically classify road networks into four classes on a visual basis. The method is implemented by generating an image of the street network (Colored Road Hierarchy Diagram), which we introduce in this paper, and classifying it using a deep convolutional neural network (ResNet-34). The model achieves an overall classification accuracy of 0.875. Nine cities around the world are selected as the study areas with their road networks acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through clustering on the percentage of each road network category. In the subsequent part of the paper, we focus on the usability of such classification: we apply our method in a case study of urban vitality prediction. An advanced tree-based regression model (LightGBM) is for the first time designated to establish the relationship between morphological indices and vitality indicators. The effect of road network classification is found to be small but positively associated with urban vitality. This work expands the toolkit of quantitative urban morphology study with new techniques, supporting further studies in the future.