unicef-schools

Deep learning pipe for school detection with UNICEF. To quickly train the school detection from high-res satellite imagery, we created a python package called Sat-Xception. Follow the following instruction to install and use the package.

Sat-Xception

Sat-Xception is a deep learning python package that utilizes pre-trained models from ImageNet. The script adapted our current open-source model for high-voltage tower detection. However, besides an pre-train Xception neural net, we also include another light-weighted pre-trained model, called MobileNet version 2, in this package.

Installation under a python environment

Currently the package has only been tested on python version 3.6.3.
To install sat-xception, transfer-learn and fine-tune an image classification model, you need to:

• set up an python environment using conda to create a virtual environment or use pyenv;
• git clone this repo;
• cd to sat-xception;
• run pip3 install -e .

The full training workflow is in the jupyter notebook here

Installation under a Nvidia docker

Use a pre-built docker images

We have a pre-built docker image developmentseed/sat-xception (for current use please pull and use geoyi/sat_exception) and you can just run:

• nvidia-docker run -v $PWD:/example -p 8888:8888 -it developmentseed/sat-xception to run a jupyter notebook;
• nvidia-docker run -v $PWD:/example -it developmentseed/sat-xception /bin/bash to run the training with CLI;

Train

You have two way to train the model:

• run our prepared jupyter notebook; or
• train the model with CLI.

Training dataset

We organize the training dataset in such a directory order:

└── main_model/
    ├── train/
           ├── not-school/
           ├── school/
    └── test/
           ├── not-school/
           ├── school/

If you want to test out Sat-Xception, our training dataset is stored at S3 bucket: s3://project-connect-nana-share/phase_2/.

After the sat_xception installed successfully, you can now run:

sat_xception train -model=xception -train=train -valid=test

to train and transfer learn the school detection with train data directory and validation data directory test.

Prediction and large scale model inference

To make a prediction over a large amount of satellite image tiles is pretty challenging. We developed a open-source tool, Chip-n-Scale that run model inference at scale. More details please see the tool GitHub repo, as well as our project report.