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https://github.com/ualsg/Road-Network-Classification
https://github.com/ualsg/Road-Network-Classification
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/ualsg/Road-Network-Classification
- Owner: ualsg
- License: mit
- Created: 2021-05-03T12:35:48.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-16T11:23:44.000Z (6 months ago)
- Last Synced: 2024-06-19T07:39:25.965Z (5 months ago)
- Language: Jupyter Notebook
- Size: 21.7 MB
- Stars: 27
- Watchers: 6
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-open-transport - (code) Road Network Classification
README
# Classification of Urban Morphology with Deep Learning: Application on Urban Vitality
This repository is the official implementation of [Classification of Urban Morphology with Deep Learning: Application on Urban Vitality](https://www.sciencedirect.com/science/article/abs/pii/S0198971521001137). It includes the major codes (written in Python) involved in the paper. We also offer some tractable tutorials in Notebook to show how to use our two modules, `CRHD generator` and `Morphoindex generator`. `CRHD generator` can automatically produce Colored Road Hierarchy Diagram (CRHD) for a given urban area. `Morphoindex generator` can automatically generate both traditional morphological indices based on built environment Shapefiles and road network class probabilities based on our road network classification model.
## Requirements
To use `CRHD generator`, you need to install the requirements:
```setup
pip install osmnx
pip install geopandas
pip install matplotlib
```
To use `Morphoindex generator`, you need to install the additional requirements:
```setup
pip install tensorflow
pip install keras
pip install cv2
pip install numpy
```
If you want to use our Morphoindex generator to calculate road network class probabilities, you should also download `config.py`, `MODEL.py` and `Build_model.py` togehther with `morphoindex_generator.py`, and put them in the same filepath. Also, make sure you have downloaded our pretrained model which you can find below.
## Tutorials
To let you quickly understand how to use our tools, we prepared some easy tutorials for you to have a glance:
[CRHD generator tutorial](https://github.com/ualsg/Road-Network-Classification/blob/main/tutorials/crhd_generator_tutorial.ipynb)
[Morphoindex generator tutorial](https://github.com/ualsg/Road-Network-Classification/blob/main/tutorials/mophoindex_generator_tutorial.ipynb)
## Pre-trained Model
You can download our pretrained models here:
- [Road network classification model](https://drive.google.com/file/d/1N7T9lN4TL5r8EqduZfWv22ROZO4zp_FN/view?usp=sharing) trained on our labelled image set using ResNet-34 architecture, learning rate as 0.0005, batch size as 2.
## Results
Our model achieves the following performance on the testing set:
**Confusion matrix and ROC curves:**
## Paper
A [paper](https://doi.org/10.1016/j.compenvurbsys.2021.101706) about the work is available.
If you use this work in a scientific context, please cite this article.
Chen W, Wu AN, Biljecki F (2021): Classification of Urban Morphology with Deep Learning: Application on Urban Vitality. Computers, Environment and Urban Systems 90: 101706.
```
@article{2021_ceus_dl_morphology,
author = {Wangyang Chen and Abraham Noah Wu and Filip Biljecki},
doi = {10.1016/j.compenvurbsys.2021.101706},
journal = {Computers, Environment and Urban Systems},
pages = {101706},
title = {Classification of Urban Morphology with Deep Learning: Application on Urban Vitality},
url = {https://doi.org/10.1016/j.compenvurbsys.2021.101706},
volume = {90},
year = 2021
}
```
## Contact
[Chen Wangyang](https://ual.sg/authors/wangyang/), [Urban Analytics Lab](https://ual.sg), National University of Singapore, Singapore