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giga-global-school-mapping

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Towards global school mapping using AI & satellite images in support of Giga, a UNICEF-ITU Intitiative

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<div style="padding-top: 20px;"> </div>
<h1><a id="giga-ai" class="anchor" aria-hidden="true" href="#giga-ai"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
UNICEF Giga: AI-enabled School Mapping  </h1>

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</div>

<details open="open">
	<summary style="padding-bottom: 10px;"><h2>Table of Contents</h2></summary>
  <ol>
    <li><a href="#about-giga">About Giga</a></li>
    <li><a href="#about">About</a>
    <ul>
        <li><a href="#project-objective">Project Objective</a></li>
        <li><a href="#flow-diagram">System Flow Diagram</a></li>
        <li><a href="#giga-repos">Github Repositories</a></li>
        <li><a href="#built-with">Built With</a></li>
      </ul>
      </li>
	<li>
      <a href="#getting-started">Getting Started</a>
    </li>
    <li><a href="#file-organization">File Organization</a></li>
    <li><a href="#model-results">Model Results</a></li>
    <li><a href="#contribution-guidelines">Contribution Guidelines</a></li>
    <li><a href="#code-design">Code Design</a></li>
    <li><a href="#code-of-conduct">Code of Conduct</a></li>
    <li><a href="#license">License</a></li>
    <li><a href="#contact">Contact</a></li>
    <li><a href="#acknowledgements">Acknowledgements</a></li>
  </ol>
</details>

<h2><a id="about-giga" class="anchor" aria-hidden="true" href="#about-giga"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>About Giga</h2>

Giga is a UNICEF-ITU global initiative to connect every school to the Internet and every young person to information, opportunity, and choice. By connecting all schools to the Internet, we ensure that every child has a fair shot at success in an increasingly digital world.

<h2><a id="about" class="anchor" aria-hidden="true" href="#about"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
About</h2>

This work leverages deep learning and high-resolution satellite images for automated school mapping and is developed under Giga, a global initiative by UNICEF-ITU to connect every school to the internet by 2030.

Obtaining complete and accurate information on schools locations is a critical first step to accelerating digital connectivity and driving progress towards SDG4: Quality Education. However, precise GPS coordinate of schools are often inaccurate, incomplete, or even completely non-existent in many developing countries.  In support of the Giga initiative, we leverage computer and remote sensing data to accelerate school mapping. This work aims to support government agencies and connectivity providers in improving school location data to better estimate the costs of digitally connecting schools and plan the strategic allocation of their financial resources.

<h3><a id="project-objective" class="anchor" aria-hidden="true" href="#project-objective"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Project Objective</h3>

- Present a publicly available, end-to-end pipeline for automated school location detection from high-resolution satellite images.
- Help governments improve the quality of school location information in their national register. 
- Identify new, previously unmapped schools in way that is quick, efficient, and scalable.

<h3><a id="flow-diagram" class="anchor" aria-hidden="true" href="#flow-diagram"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
System Flow Diagram </h3>

For each school and non-school location in our dataset, we downloaded 300 x 300 m, 500 x 500 px high-resolution satellite images from Maxar with a spatial resolution of 60 cm/px.

<h3><a id="giga-repos" class="anchor" aria-hidden="true" href="#giga-repos"><svg class="octicon octicon-link" align="center" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Github Repositories </h3>

- [Giga AI-enabled School Mapping](https://github.com/unicef/giga-global-school-mapping/)
- [Giga AI School Validation](https://github.com/unicef/giga-school-mapping-validator)

<h3><a id="built-with" class="anchor" aria-hidden="true" href="#built-with"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16"gig aria-hidden="true"></path></svg></a>
Built With </h3>

- <b>ML/DL Frameworks:</b>  Scikit-learn, Pytorch
- <b>Programming Language:</b>  Python
- <b>Geospatial Libraries:</b>  GeoPandas, Rasterio, Fiona, GDAL

<h2><a id="getting-started" class="anchor" aria-hidden="true" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Getting Started</h2>

### Setup
```sh
conda create -n <env_name> python==3.10.13
conda activate <env_name>
pip install -r requirements.txt
```
Add your present working directory (pwd) to your Python path environment variable by adding this line to `~/.profile`:
```sh
export PYTHONPATH=$(pwd)
```
Add the conda environment to jupyterlab:
```sh
conda install ipykernel
ipython kernel install --user --name=<env_name>
```

### Install GDAL/OGR: Follow these [instructions](https://ljvmiranda921.github.io/notebook/2019/04/13/install-gdal/).

<h2><a id="code-design" class="anchor" aria-hidden="true" href="#code-design"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Code Design</h2>

This repository is divided into the following files and folders:
- `configs/`: contains the configuration files (data configs, satellite image configs, model configs, etc.)
- `notebooks/`: contains all Jupyter notebooks for exploratory data analysis.
- `utils/`: contains utility methods for loading datasets, building model, and performing training routines.
- `src/`: contains scripts runnable scripts for automated data cleaning and model training, evaluation, and deployment.

## Data Download 
To download the relevant datasets, run either of the following:
- `notebooks/01_data_download.ipynb`
- `python src/data_download.py`:
```s
usage: data_download.py [-h] [--config CONFIG] [--profile PROFILE]

Data Download
options:
  -h, --help         show this help message and exit
  --config CONFIG    Path to the configuration file
  --profile PROFILE  Path to the profile file
```

#### Sample usage
```s
python src/data_download.py --config="configs/data_configs/data_config_ISO_AS.yaml" --profile="configs/profile.share"
```

#### Outputs
-  School files are saved to `data/vectors/<project_name>/school/` 
- Non-school files are save to `data/vectors/<project_name>/non_school/`.

## Data Preparation
The data cleaning script can be found in `src/data_preprocess.py`:
```sh
usage: data_preprocess.py [-h] [--config CONFIG] [--creds CREDS] [--clean_pos CLEAN_POS] [--clean_neg CLEAN_NEG]

Data Cleaning Pipeline

options:
  -h, --help                      show this help message and exit
  --config CONFIG                 Path to the configuration file
  --sat_config SAT_CONFIG         Path to the satellite config file
  --sat_creds SAT_CREDS           Path to the satellite credentials file
  --clean_neg CLEAN_NEG           Clean negative samples (bool, default: False)
  --sources SOURCES [SOURCES ...] Sources (string, default: unicef, osm, overture)
  --imb_ratio IMB_RATIO           Imbalance ratio for negative samples (int, default: 2)
```

### Cleaning School Samples
Run data cleaning for the positive samples.

#### Sample usage
```s
python src/data_preprocess.py --config="configs/data_configs/data_config_ISO_AF.yaml" --sat_creds="configs/sat_configs/sat_creds.yaml" --sat_config="configs/sat_configs/sat_config_500x500_60cm.yaml" --clean_neg=False
```
#### Manual Data Cleaning ✨
Manually inspect and clean the satellite images using `notebooks/03_sat_cleaning.ipynb`.

This will add a `validated` column (or field) to the `<iso>_clean.geojson` file indicating which images to retain (0) and discard (-1) for model training.

#### Outputs
-  Vector outputs are saved to `data/vectors/<project_name>/school/clean/<iso_code>_clean.geojson`.  
- Satellite images are saved to `data/rasters/500x500_60cm/<project_name>/<iso_code>/school/`

### Cleaning Non-school Samples
Run data cleaning for the negative samples. This will sample up to 2x the number of (clean) school data points.

#### Sample usage
```s
python src/data_preprocess.py --config="configs/data_configs/data_config_ISO_AF.yaml" --sat_creds="configs/sat_configs/sat_creds.yaml" --sat_config="configs/sat_configs/sat_config_500x500_60cm.yaml" --clean_neg=True
```

#### Outputs
- Vector outputs are saved to `data/vectors/<project_name>/non_school/clean/<iso_code>_clean.geojson`. 
- Satellite images are saved to `data/rasters/500x500_60cm/<project_name>/<iso_code>/non_school/`.

## Model Training
To train the computer vision models, run:
```s
sh train.sh
```

Alternatively, you can run `python src/train_cnn.py`:
```s
usage: train_cnn.py [-h] [--config MODEL_CONFIG] [--lr_finder LR_FINDER] [--iso ISO [ISO ...]]

Model Training
options:
  -h, --help              show this help message and exit
  --config MODEL_CONFIG   Path to the model configuration file
  --lr_finder LR_FINDER   Learning rate finder (bool, default: False)
  --iso ISO [ISO ...]     ISO 3166-1 alpha-3 codes
```

#### Sample usage
```sh
python src/train_model.py --config="configs/cnn_configs/convnext_small.yaml" --iso=MNG; 
```

#### Outputs
Model results will be saved to `exp/<project_name>/<iso_code>_<model_name>/` (e.g. `exp/GIGAv2/MNG_convnext_small/`)

To evaluate the VSC model ensemble, run `05_model_evaluation.ipynb`.

## CAM Evaluation
To determine the best CAM method, run `src/cam_evaluate.py`:
```s
usage: cam_evaluate.py [-h] [--iso_code ISO_CODE] [--percentile PERCENTILE] [--model_config MODEL_CONFIG]

CAM Evaluation

options:
  -h, --help                  show this help message and exit
  --iso_code ISO_CODE         ISO 3166-1 alpha-3 code
  --percentile PERCENTILE     Percentile (float, default: 90)
  --model_config MODEL_CONFIG Model config file (str, optional)
```

#### Sample usage
```sh
python src/cam_evaluate.py --iso_code="MNG"
```

#### Outputs
The output will be saved in `exp/<project_name>/<iso_code><best_model_name>/cam_results.csv`.

## Download Nationwide Satellite Images
To download nationwide satellite images, run `src/sat_batch_download.py`. 
```sh
usage: sat_batch_download.py [-h] [--data_config DATA_CONFIG] [--sat_config SAT_CONFIG] [--sat_creds SAT_CREDS] [--iso_code ISO_CODE] [--adm_level ADM_LEVEL] [--sum_threshold SUM_THRESHOLD] [--buffer_size BUFFER_SIZE] [--spacing SPACING]

Satellite Image Download

options:
  -h, --help                    show this help message and exit
  --data_config DATA_CONFIG     Path to the data configuration file
  --sat_config SAT_CONFIG       Path to the satellite configuration file
  --sat_creds SAT_CREDS         Path to the satellite credentials file
  --iso_code ISO_CODE           ISO 3166-1 alpha-3 code
  --adm_level ADM_LEVEL         Administrative level (string, default ADM2)
  --sum_threshold SUM_THRESHOLD Pixel sum threshold (int, default 5)
  --buffer_size BUFFER_SIZE     Buffer size (int, default 150)
  --spacing SPACING             Sliding window spacing (int, default 150)
```

#### Sample usage
```sh
python src/sat_batch_download.py --data_config="configs/data_configs/data_config_ISO_AS.yaml" --sat_config="configs/sat_configs/sat_config_500x500_60cm.yaml" --sat_creds="configs/sat_configs/sat_creds.yaml" --iso_code=MNG;
```

#### Outputs
The satellite images are saved to `output/<iso_code>/images/`.

## Nationwide Model Deployment
For model prediction, run `python src/sat_predict.py`:
```sh
usage: sat_predict.py [-h] [--data_config DATA_CONFIG] [--sat_config SAT_CONFIG] [--sat_creds SAT_CREDS] [--shapename SHAPENAME] [--iso_code ISO_CODE]

Model Prediction

options:
  -h, --help                    show this help message and exit
  --data_config DATA_CONFIG     Data config file
  --sat_config SAT_CONFIG       Maxar config file
  --sat_creds SAT_CREDS         Credentials file
  --shapename SHAPENAME         Model shapename
  --iso_code ISO_CODE           ISO 3166-1 alpha-3
```

#### Sample usage
```sh
python src/sat_predict.py --data_config="configs/data_configs/data_config_ISO_AF.yaml" --sat_config="configs/sat_configs/sat_config_500x500_60cm.yaml" --sat_creds="configs/sat_configs/sat_creds.yaml" --iso_code=RWA;
```

#### Outputs
The outputs are saved to `output/<iso_code>/results/<project_name>/cams/<iso_code>_<best_model_name>_<cam_method>.geojson`.

<h2><a id="file-organization" class="anchor" aria-hidden="true" href="#file-organization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
File Organization</h2>

For the detailed documentation on file organization, see the [File Organization Documentation](https://github.com/unicef/giga-global-school-mapping/blob/master/File-Organization.md).

<h2><a id="model-results" class="anchor" aria-hidden="true" href="#file-organization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Model Results</h2>

For more info on the model results, see the [Model Results Documentation](https://github.com/unicef/giga-global-school-mapping/blob/master/Model-Results.md).

<h2><a id="contribution-guidelines" class="anchor" aria-hidden="true" href="#contribution-guidelines"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Contribution Guidelines</h2>

Thank you for considering contributing to Giga! We value your input and aim to make the contribution process as accessible and transparent as possible. Whether you're interested in reporting bugs, discussing code, submitting fixes, proposing features, becoming a maintainer, or engaging with the Giga community, we welcome your involvement.

[Click here for detailed Contribution Guidelines](https://github.com/unicef/giga-global-school-mapping/blob/master/Contribution-Guidelines.md)

<h2><a id="code-of-conduct" class="anchor" aria-hidden="true" href="#code-of-conduct"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Code of Conduct</h2>

At Giga, we're committed to maintaining an environment that's respectful, inclusive, and harassment-free for everyone involved in our project and community. We welcome contributors and participants from diverse backgrounds and pledge to uphold the standards.

[Click here for detailed Code of Conduct](https://github.com/unicef/giga-global-school-mapping/blob/master/CODE_OF_CONDUCT.md)

<h2><a id="contact" class="anchor" aria-hidden="true" href="#contact"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Contact</h2>

Applied Science AI-enabled School Mapping Team:
- Isabelle Tingzon: itingzon@unicef.com 
- Ivan Dotu Rodriguez: jdoturodriguez@unicef.com

Giga Website: https://giga.global/contact-us/

<h2><a id="acknowledgements" class="anchor" aria-hidden="true" href="#acknowledgements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"></path></svg></a>
Acknowledgments💜</h2> 
Global high-resolution satellite images (60 cm/px) from Maxar made available with the generous support of the US State Department. We are also grateful to Dell for providing us with access to High Performance Computing (HPC) clusters with NVIDIA GPU support.

## Citation
If you find this repository useful, please consider giving a star ⭐ and citation 🦖:
```
@misc{tingzon2024largescaleschoolmapping,
      title={Large-scale School Mapping using Weakly Supervised Deep Learning for Universal School Connectivity}, 
      author={Isabelle Tingzon and Utku Can Ozturk and Ivan Dotu},
      year={2024},
      eprint={2412.14870},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.14870}, 
}
```

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