Advanced Landslide identifications, classifications, and prevention in Hong Kong using Deep Learning
Project Description

Anticipating the imminent threats of landslip hazards triggered by extreme precipitations brought about by global warming, the HKSAR government intends to upgrade the city's climate resilience. Local real-time monitoring data gathered economically at scale is the key. However, data interpenetration can be the most time consuming endeavor. Hence, we have to use the advanced technology to tackle this long-term issue in Hong Kong. In this project, student will have hands-on experience on applying the cutting-edge Deep Learning algorithms on landslide identifications, classifications, and prevention.

Supervisor
WANG Yu-Hsing
Quota
5
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

To apply the Deep Learning algorithms on landslide identifications, classifications, and prevention. Therefore, students need to have passion on programming.

Applicant's Learning Objectives

Student will have hands-on experience on applying the cutting-edge Deep Learning algorithms on landslide identifications, classifications, and prevention.

Complexity of the project
Challenging