Update README.md

README.md

@@ -24,32 +24,63 @@ size_categories:
 
 Repository for the tabular portion of the [Global Streetscapes dataset](https://ual.sg/project/global-streetscapes/) by the [Urban Analytics Lab (UAL)](https://ual.sg/) at the National University of Singapore (NUS).
 
-Content breakdown:
-* `data/` (37 GB)
-  * 21 `csv` files with 346 unique features in total and 10 million rows each to characterise the 10 million street-level images in our dataset
-* `manual_labels/` (23 GB)
-  * `train/`
-    * 8 `csv` files of manual labels for training computer vision models to classify 8 different [contextual characteristics](https://github.com/ualsg/global-streetscapes?tab=readme-ov-file#manually-labelled-subset-for-benchmarking) of a street view image, along with other metadata such as the image's location, city, file path etc. 
-  * `test/`
-    * 8 `csv` files of manual labels for model testing, along with other metadata such as the image's location, city, file path etc.
-  * `img/`
-    * 7 `tar.gz` files containing all images used for training and testing
-* `models/` (2.8 GB)
-  * 8 `ckpt` files storing the trained models
-* `cities688.csv` contains basic information for the 688 cities included in the dataset, such as population, continent, image count etc.
-* `info.csv` overviews the content of each `csv` file in `/data` and explains the 346 features 
-
-This repository has a total size of about 62 GB.
-
-Please follow this [guide](https://huggingface.co/docs/huggingface_hub/guides/download) from huggingface for download instructions. Please avoid using 'git clone' to download the repo as Git stores the files twice and will double the disk space usage to 124+ GB.
-
-We have also provided a script `download_folder.py` to download one folder from this dataset, instead of just a single file or the entire dataset.
+## Content Breakdown
+```
+Global Streetscapes (62+ GB)
+├── data/ (37 GB)
+│   ├── 21 CSV files with 346 unique features in total and 10M rows each
+├── manual_labels/ (23 GB)
+│   ├── train/
+│   │   ├── 8 CSV files with manual labels for contextual attributes (training)
+│   ├── test/
+│   │   ├── 8 CSV files with manual labels for contextual attributes (testing)
+│   ├── img/
+│       ├── 7 tar.gz files containing images for training and testing
+├── models/ (2.8 GB)
+│   ├── Trained models in checkpoint format
+├── cities688.csv
+│   ├── Basic information for the 688 cities including population, continent, and image count
+├── info.csv
+    ├── Overview of CSV files in `/data/` with description of each feature
+```
+
+## Download Instructions
+Please follow this [guide](https://huggingface.co/docs/huggingface_hub/guides/download) from Hugging Face for download instructions. Please avoid using 'git clone' to download the repo as Git stores the files twice and will double the disk space usage to 124+ GB.
+
+We have also provided a script `download_folder.py` to download a specifc folder from this dataset, instead of just a single file or the entire dataset.
 
 To download the imagery portion (10 million images, ~6TB), please follow the code and documentation in our [GitHub repo](https://github.com/ualsg/global-streetscapes).
 Our [Wiki](https://github.com/ualsg/global-streetscapes/wiki/2-Download-images) contains instructions and a demo on how to filter the dataset for a subset of data of your interest and download the image files for them.
 
+## Contribution Guide
+We welcome contributions to this dataset! Please follow these steps:
+
+1. **Propose changes**:
+   - Open a [discussion](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions) in the repository to describe your proposed changes or additions.
+   - We will revert with specifics on how we would like your contributions to be incorporated (e.g. which folder to add your files), to maintain a neat organisation.
+
+2. **File naming**:
+   - Use meaningful and descriptive file names.
+
+3. **Submit changes**:
+   - Fork the repository, implement your changes, and submit a pull request (PR). In your PR, include an informative description of your changes (e.g. explaining their structure, features, and purpose) and how you would like to be credited.
+
+Upon merging your PR, we will update the `Changelog` and `Content Breakdown` on this Dataset Card accordingly to reflect the changes and contributors.
+
+For any questions, please contact us via [Discussions](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions).
+
+## Changelog
+
+**YYYY-MM-DD**
+
+## Read More
+
 Read more about this project on [its website](https://ual.sg/project/global-streetscapes/), which includes an overview of this effort together with the background, [paper](https://doi.org/10.1016/j.isprsjprs.2024.06.023), examples, and FAQ.
 
+A free version (postprint / author-accepted manuscript) can be downloaded [here](https://ual.sg/publication/2024-global-streetscapes/).
+
+## Citation
+
 To cite this work, please refer to the [paper](https://doi.org/10.1016/j.isprsjprs.2024.06.023):
 
 Hou Y, Quintana M, Khomiakov M, Yap W, Ouyang J, Ito K, Wang Z, Zhao T, Biljecki F (2024): Global Streetscapes — A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics. ISPRS Journal of Photogrammetry and Remote Sensing 215: 216-238. doi:[10.1016/j.isprsjprs.2024.06.023](https://doi.org/10.1016/j.isprsjprs.2024.06.023)
@@ -65,6 +96,4 @@ BibTeX:
  volume = {215},
  year = {2024}
 }
-```
-
-A free version (postprint / author-accepted manuscript) can be downloaded [here](https://ual.sg/publication/2024-global-streetscapes/).
+```