  Post-hazard reconnaissance for natural disasters (e.g., earthquakes) is
important for understanding the performance of the built environment, speeding
up the recovery, enhancing resilience and making informed decisions related to
current and future hazards. Natural language processing (NLP) is used in this
study for the purposes of increasing the accuracy and efficiency of natural
hazard reconnaissance through automation. The study particularly focuses on (1)
automated data (news and social media) collection hosted by the Pacific
Earthquake Engineering Research (PEER) Center server, (2) automatic generation
of reconnaissance reports, and (3) use of social media to extract post-hazard
information such as the recovery time. Obtained results are encouraging for
further development and wider usage of various NLP methods in natural hazard
reconnaissance.
