deepLDB -- a machine-learning-based Landslide database and modeling system
This project is one of the 20 grantees of the Google AI Impacts Challenge in 2019. The goals of the project are to (1) create a landslide database focusing on events there were not previously reported in the news; and (2) build a model that improves our predictive capability of the landslide hazards. We will extensively leverage modern AI technologies and big datasets to achieve these goals. The project starts in June 2019. The mission of this project is to minimize the societal impact of landslide hazards with better predictive capability. This site supports the project by providing progress tracking documents (available to project personnel), host wiki materials, links to data, and tutorials, and announce news, updates and results gallery.
Our team is based in the department of Civil and Environmental Engineering & the department of Computer Science and Engineering at The Pennsylvania State University. The principal investigator of the project is Dr. Chaopeng Shen, Associate Professor in Civil Engineering and director of the Multi-scale Hydrology, Processes and Intelligence group. Co-investigators are Dr. Tong Qiu, Associate Professor in Civil Engineering and Dr. Daniel Kifer, Associate Professor in Computer Science. This project will involve postdoctoral researcher, graduate students, and undergraduate students. We fortunately also have in our squad Google volunteers Skye Wang, Sarah Tran and Mary Witkowski.
We are open to collaboration with any colleagues interested in pooling together data and other assets. We are all trying to get to the same goal that is to reduce landslide hazards.
Phase 2 data collection instructions announced.
From left to right: Googler Sarah Tran, Googler Skye Wang, Chaopeng Shen, Tong Qiu and Mary Witkowski at the Google Launchpad program, May 2019 in San Francisco.
Here is a description of the project personnel.