The use of crowdsourced volunteers to analyze remote sensing imagery is a relatively new damage assessment approach, developed in the wake of the 2008 Sichuan earthquake, and formalized during the 2010 Haiti and 2011 New Zealand earthquakes. This approach is enabled by the advent of Web 2.0 technologies and the ubiquity of free remote sensing images that are synoptic with high spatial-, spectral- and temporal-resolutions. The demonstrated benefit was a speedup by a factor of two or three in the delivery of damage estimates. However, the success of this manual crowdsourced approach for damage assessment is dependent upon the size and reliability of the crowd. This research will focus on a new framework called BACKBOnE (Building A Crowdsourced Knowledge Base of Extreme Events) for extreme event damage assessment utilizing remotely sensed images that automatically finds and classifies damages, and builds a data-driven knowledge base of damage characteristics that can be reused during future events. BACKBOnE is a transformation of the manual crowdsourced approach for damage assessment and is unprecedented in the disasters community, combining the power of crowdsourcing with state-of-the-art methods from computer science and image processing. It replaces the manual effort with automated methods for object-based change detection and classification that increase the speed, reduce the cost of damage assessment, and scale well to increases in data volume. It shifts the crowd from its task of manual annotation to quality assurance feedback on the performance of automated methods via crowdsourced active learning. This improves assessment accuracy, while decoupling the framework's success from the size and reliability of the crowd because feedback is solicited from annotators scored favorably, and only on difficult cases. It also incorporates a multitude of remote sensing products and performs data fusion to unify their outputs into a common map of damage. This is a must-have characteristic of next-generation damage assessment as data volumes and products proliferate. The use of diverse data products, particularly imagery from high spatial resolutions and non-visible bands that are less sensitive to weather and solar illumination, will better discriminate certain damage types.
The broader impact of the research is the reduction of the overall human and financial cost of extreme events by contributing new methods for rapid and accurate damage estimates used for Post-Disaster Needs Assessment (PDNA). The curation of a knowledge base builds effective models quickly when a disaster strikes, refines damage predictions in event simulations that assess vulnerability, and fosters better land-use planning that encourages the growth of disaster resilient communities. The work also includes a web-based damage assessment simulator that maps remotely sensed earthquake images from recent earthquake events to engage the greater public in disaster mitigation. In addition, the investigators will actively recruit graduate and undergraduate students from under-represented groups and mentor them within a multi-disciplinary collaboration. Machine learning students will learn about remote sensing and damage assessment, and geoengineering students will learn fundamentals of machine learning and statistical data analysis.
