Open AI Caribbean Challenge: Mapping Disaster Risk from Aerial Imagery

Can you predict the roof material of buildings from drone imagery? Leverage aerial imagery in St. Lucia, Guatemala, and Colombia to more accurately map disaster risk at scale. #disasters

$10,000 in prizes
dec 2019
1,419 joined


MathWorks, makers of MATLAB and Simulink software, is sponsoring this challenge and the bonus award for Top MATLAB user. They're also supporting participants by providing complimentary software licenses and learning resources.

To request your complimentary license, go to the MathWorks site, click the “Request Software” button, and fill out the software request form.

MATLAB code to help you get started with the competition dataset is available on GitHub along with a walkthrough blog post.

During the course of the competition, the MathWorks Student Competition team is also available to support participants. MathWorks Student Competition team supports competitions from all over the world in the areas such as aerospace, automotive, AI & robotics, among others. Neha Goel, Technical Lead for Data Science & Deep Learning Competitions, will be your point of contact. You can learn more how to implement Deep Learning models in your competitions by checking out her blog.

If you have questions, email MathWorks at or post on the DrivenData community forum.

Additional resources

Below are some links to learn about MATLAB, Deep Learning Toolbox, and other useful resources for this competition.


  1. MATLAB Onramp
  2. Deep Learning Onramp
  3. Computer Vision Video Tutorials

General Resources

  1. Deep Network Designer App
  2. 8 MATLAB Cheat Sheets for Data Science
  3. Machine Learning Challenges: Choosing the Best Classification Model and Avoiding Overfitting
  4. Pretrained Deep Neural Networks


  1. Deep Learning with Images – Examples
  2. Deep Learning Classification of Large Multiresolution Images
  3. Image Processing Toolbox - Examples


  1. Machine Learning with MATLAB: Getting Started with Classification
  2. Introduction to Machine Learning
  3. Applied Machine Learning
  4. Deep Learning with MATLAB