The goal of the challenge is to develop a machine learning model to identify and detect “damaged” and “un-damaged” coastal infrastructure (residential and commercial buildings), which have been impacted by natural calamities such as hurricanes, cyclones, etc. Participants will be given pre- and post-cyclone satellite images of a site impacted by Hurricane Maria in 2017 and build a machine learning model, designed to detect four different objects in a satellite image of a cyclone impacted area:

1. Undamaged residential building
2. Damaged residential building
3. Undamaged commercial building
4. Damaged commercial building

• We are among the Top 10 Global Semi-Finalist of EY Open Science Data Challenge 2024 🎉🥳
  
• We ranked 11 out of 222 teams worldwide on the leaderboard (Top 5%) 🌍🏆
  
• In terms of evaluation score, we rank 4th, tying with other impressive competing teams! 🤩
• Meanwhile, we ranked 1st out of 22 teams in Malaysia! 🏅

EY-challenge-2024
├── our-best-runs                       (proof of our experiment that yields the highest mAP)
│   ├── detect
│   │   ├── predict                     
│   │   ├── train                       
├── additional-dataset.zip              (additional dataset)
├── best-trained-model.pt               (best trained model which we used for submission, mAP 0.51)
├── challenge_1_submission_images.zip   (just the zip file of EY Challenge Phase 1 test images)
├── labelled-dataset.zip                (labelled dataset)
├── Model-development-notebook.ipynb    (to train the model)
├── pretrained-on-msft-puerto-rico      (models pretrained on Microsoft Building Footprint -> Puerto Rico dataset)
├── requirements.txt                    (dependencies requirement)
├── Validation-notebook.ipynb           (for Phase 1 submission)

# Clone the repo
git clone https://github.com/yjwong1999/EY-challenge-2024.git
cd EY-challenge-2024

# Create conda environment
conda create --name ey-challenge python=3.8.10 -y
conda activate ey-challenge

1. Use Model-development-notebook.ipynb to train the model following our pipeline. If you cant download the additional-dataset.zip due to filesize error, than you can use Model-development-notebook (backup).ipynb as an alternative
2. Use Validation-notebook.ipynb to generate submission.zip

1. Our submitted content-package can be found in this link. You can find all of our datasets here as well