About This Course
Imagine coordinating a response after the chaos of a hurricane or the challenges of a famine lasting years, these big problems require big data to solve. With airplanes and satellites, we collect mountains of data of affected regions but who looks at this data? How do we turn this data into a physical response? The program’s goal is for participants to explore, leverage, and transform open source information and imagery collected from drones, airplanes, helicopters, and satellites to generate actionable intelligence to support a disaster or humanitarian response. Students will be exposed to three main components: 1) feature extraction from raw data, 2) classification via machine learning techniques, and 3) data products for decision makers. The program will explore tools and techniques using real world operational data collected from across the globe.
This new BWSI Remote Sensing program will offer students the opportunity to explore the exciting intersection of data science and crisis response. The program consists of two components: (1) online course from January to May, open to all interested and committed students; and (2) a four-week summer program at MIT campus in Cambridge, MA. During the course, the students will learn to understand the basics of Python, Git, machine learning, and image processing through a series of online teaching modules. Students will explore real world datasets ranging from drone imagery of regions to disaster imagery collected by the human volunteers of the Civil Air Patrol. By participating in the online and/or onsite portion of the program, students will develop experience in an area of data science that is poised to play a critical role in understanding our world.
Jeff Liu, Ph.D., Lead Instructor
Jeff Liu is a member of the Humanitarian Assistance and Disaster Relief Systems Group in the Homeland Protection and Air Traffic Control Division at MIT Lincoln Laboratory. Dr. Liu joined the Laboratory in 2019, and has been working on applications of computer vision and GIS for humanitarian assistance and disaster relief applications. He co-created the Remote Sensing for Disaster Relief BWSI course with his co-instructor, Andrew Weinert, in 2019. He received his Ph.D. in Civil Engineering and Computation from MIT, where he wrote his dissertation on information and its effects on road traffic networks. He also holds an S.M. from MIT in Computation for Design and Optimization, and a B.S.E. in Engineering Physics from the University of Michigan. In his free time, Jeff likes to listen to and make music, play video and tabletop games, ride bicycles, and make art.
Andrew Weinert, Emeritus Instructor
Andrew Weinert is a member of the Humanitarian Assistance and Disaster Relief Systems Group in the Homeland Protection and Air Traffic Control Division at MIT Lincoln Laboratory. Mr. Weinert joined the Laboratory in 2009, focusing on drone airspace integration and public safety information systems while supporting two R&D 100 Award winning programs. His master's thesis focused on optimization of aircraft avoidance using information theory and parallel processing techniques. In recent years for the public safety community, he has supported NIST R&D roadmaps, produced actionable intelligence from open-source and social media data, and optimized allocation of drones for disaster and incident response. Mr. Weinert is currently serving as the technical lead for a NIST Public Safety Innovation Accelerator Program to generate representative public safety video datasets and leverage edge computing to improve tactical communications. He received a MS in Electrical and Computer Engineering at Boston University and a BS in Security and Risk Analysis with minors in Information Science Technology for Aerospace Engineering and Natural Science from the Pennsylvania State University. Mr. Weinert also serves on the Pennsylvania State University College of Information Sciences Alumni Society Board, holds a FAA remote pilot certificate (Part 107), and a FCC amateur radio license.