Transforming Aerial Reconnaissance Data of Pavement Infrastructure into Knowledge for Better Response to Natural Disasters

The increase in the frequency of geohazards and extreme weather events in recent times is alarming. Most of these events such as hurricanes, wildfires, floods, avalanches, earthquakes, and other natural disasters are dependent on many factors that cannot be controlled by human beings; however, they can be well prepared for with proper planning. Landslides, washouts, and slope failures triggered by natural disasters cause tremendous damage to human lives as well as infrastructure assets. Many states across the United States have established emergency operations centers (EOCs) to coordinate the emergency response operations. These operations include various activities ranging from monitoring critical infrastructure areas to creating public awareness during and after natural disasters. Certain natural disasters can be overwhelming that these agencies become shorthanded, and hence they need to prioritize rescue and recovery operations over reconnaissance surveys of infrastructure conditions. The rise in the applications of unmanned aerial vehicles (UAVs) or drones for monitoring infrastructure and emergency responses has been propelled by recent advancements in aerial vehicle platforms and compact sensors. This study presents an efficient way to transform the aerial imagery data of infrastructure into knowledge for quick and efficient responses to these unexpected natural calamities. Aerial data collected at two case study locations affected by natural disasters were analyzed and transformed into useful knowledge about the existing pavement conditions. This can help the EOCs in efficiently managing and analyzing the pavement infrastructure conditions to assist in the decision-making process. These practices are expected to contribute toward planning response and recovery tasks to facilitate the process of bringing back normalcy to the communities.