Speaker "Farshad Saberi Movahed" Details Back



Natural Disaster Damage Assessment from Satellite Imagery


The assessment of building damage after a natural disaster is an analytical bottleneck in the post-disaster workflow. Currently, the response and recovery efforts often require manual review of the aerial imagery of the areas impacted by the disaster. Such reviews might take days or weeks depending on the type of disaster and adversities caused by it. The goal of this project is to develop a solution to automate the analysis of aerial imagery. To reach our goal, we have built a pipeline for large volume data processing and applied a deep learning framework to analyze and classify the aerial images.


Farshad is a machine learning engineer / data scientist at REI Systems, where he builds machine learning systems to solve business problems of REI Systems' clients. He also worked as a data scientist at Cisco. Farshad received his PhD degree in Materials Science and Engineering with concentration in Computational Sciences from North Carolina State University.