The Disaster Damage Detection team worked on one of three projects from the 2018 Data Science for Social Good summer fellowship at the University of Washington eScience Institute. The goal of this project is to use post hurricane satellite imagery data to train object detection models to automatically detect damages from satellite images after hurricanes to facilitate the damage assessment process for emergency managers.
This website catalogs the groups work to:
Gather, clean and prepare training datasets with annotations for damaged buildings and test datasets
Train the machine learning algorithm using the training data
Test the model on a test dataset
Project Lead: Youngjun Choe, Assistant Professor of Industrial & Systems Engineering and Director of the Disaster Data Science Lab, Aerospace & Engineering Research Building, University of Washington
Data Scientist Lead: Valentina Staneva, Senior Data Scientist, eScience Institute