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 8th globally out of a total of 11,000 registrants 🌍🏆
  
• Meanwhile, we ranked 1st out of 22 teams in Malaysia! 🏅

@article{Wong2024,
   author = {Yi Jie Wong and Yin Loon Khor and Liu Ziwei},
   doi = {10.36227/TECHRXIV.172963135.56918790/V1},
   institution = {Techrxiv},
   journal = {Authorea Preprints},
   month = {10},
   publisher = {Authorea},
   title = {Automating Coastal Vulnerability Assessment: AI-Driven Geospatial Analysis via Building Damage Detection},
   url = {https://www.authorea.com/users/844381/articles/1233489-automating-coastal-vulnerability-assessment-ai-driven-geospatial-analysis-via-building-damage-detection},
   year = {2024},
}