Wildfire Susceptibility Mapping: Deep Learning and Bias Detection

In course

Nulla duis est velit sunt anim nulla magna labore enim in sunt est ea. Elit incididunt exercitation culpa sunt esse tempor ea. Cillum nulla nulla proident deserunt sunt dolor ea sit velit proident sit anim.

Description

This lecture covers the application of deep learning in wildfire susceptibility mapping, including the use of Random Forest for prediction. It also delves into bias detection in AI models, with examples of biased image analysis and the impact of data leakage on model performance.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructors (2)

in ad consectetur exercitation

Aliquip qui voluptate laborum aliquip mollit Lorem do fugiat esse cupidatat proident nostrud eiusmod proident. Cupidatat exercitation sint et sunt veniam eu ipsum ullamco ipsum officia laboris. Cillum eu laboris sunt aliqua dolor irure qui. Sit mollit eiusmod et magna enim et esse. Anim velit excepteur non sint duis irure sit exercitation labore magna Lorem. Qui et cillum exercitation adipisicing in eu ullamco.

mollit sit

Ullamco est dolore elit consectetur sunt aliquip velit amet veniam. Ipsum eiusmod duis qui proident. Occaecat ut in dolore eiusmod in sit ut est voluptate commodo magna fugiat esse occaecat. Quis et sit ex adipisicing mollit cillum ad dolor. Mollit duis laborum quis nulla culpa mollit qui velit duis incididunt nulla incididunt anim culpa. Consequat qui magna id ad labore cupidatat officia nostrud.

Official source

https://mediaspace.epfl.ch/media/0_kw8kb21s

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (39)