Team Location: Jet Propulsion Laboratory, Pasadena, California
Pasadena, CA California: – Landsat 5 imagery acquired on February Feb. 9, 2008, was classified and regrouped into two categories: structural and non-structural areas. NASA Gulfstream G3 UAVSAR data acquired on February Feb. 15, 2007, and February Feb. 18, 2008, was applied to ARIA’s change-detection algorithm to produce a damage proxy map, shown in red on the map. The classification and footprint masks are used to identify false positives, the non-structural changes that the radar detected, in the damage proxy map. Image Credit: U.S. Disasters Team, NASA DEVELOP National Program.
Authors:
Judy Cheng (University of California at Los Angeles), Project Lead
Sara Lafia (California State Polytechnic University at Pomona)
Rodell Allan Zorilla (University of California at Los Angeles)
Mentors/Advisers:
Sang-Ho Yun, Ph.D. (Jet Propulsion Laboratory/California Institute of Technology)
Abstract:
Year after year, natural disasters such as earthquakes, fires, floods and hurricanes claim the lives of many people and cause economic losses. In the aftermath of such events, accurate and comprehensive assessment of damage is needed for rapid-rescue response to minimize loss of life and to begin the recovery process.
A damage-detection algorithm developed by the Advanced Rapid Imaging and Analysis (ARIA) team at NASA’s Jet Propulsion Laboratory (JPL) and the California Institute of Technology (Caltech) uses Interferometric Synthetic Aperture Radar (InSAR) coherence change to produce damage proxy maps (DPM) of an affected region. Once the initial DPM is produced, it is handed off to responding agencies, such as GISCorps and the Esri Disaster Response Program. However, since false positives such as vegetation or anthropogenic changes are detected and included in the initial DPM, it is necessary to validate and to verify true positives, which show structural damages. Currently, GISCorps recruits volunteers for this task who individually validate a DPM manually with their own choice of software. This is a time-consuming process that can take days to complete. This project developed Geographic Information System (GIS) tools to standardize and automate the validation process with the objective of reducing it to hours. The results of this study helped improve the format-conversion method, thus improving the efficiency and response time to natural disasters.