TEES just concluded a three day Summer Institute on Floods (July 26-28, 2015), which was hosted by our “mother” center, the Center for Emergency Informatics. The Summer Institute focuses on the data-to-decision problems for a particular type of disaster. This year’s Summer Institute was the second in two-part series on floods and brought together representatives from 12 State agencies, 15 universities, and 5 companies for two days of “this is what we did during the Texas floods” and one day of “this is what we could do or do better” experimentation with small unmanned aerial vehicles, crowd sourcing, computer vision, map-based visualization packages, and mobile phone apps for common operating pictures and data collection.
A portion of the Summer Institute focused strictly on UAVs. The session was organized by the Lone Star UAS Center and the TEES Center for Robot-Assisted Search and Rescue (CRASAR), both of whom flew during the Texas floods.
The UAV field exercises spanned four scenarios witnessed during the floods:
- Search of cars and other vehicles swept away by a storm surge for trapped victims;
- Search for missing persons who may have fled campsites or been swept down river;
- Assessment of damage to power lines and transformers and presence of standing water which prevents restoration of power infrastructure; and
- Assessment of damage to houses and estimates of household debris, which is critical to insurance companies estimating damage and agencies such as the American Red Cross in projecting social impacts.
The exercises were staged at the 1,900 acre TAMU Riverside Campus with one fixed wing and three types of rotorcraft flown by CRASAR and David Kovak, a member of CRASAR’s Roboticists Without Borders program.
A major finding out of the institute was the realization that while State agencies are adopting UAVs, the agencies can’t process all the imagery coming in. For example, a single 20-minute UAV flight by CRASAR at the floods produced over 800 images totaling 1.7GB. There were over a dozen platforms flying daily for two weeks as well as Civil Air Patrol and satellite imagery. Each image has to be viewed manually for signs of survivors, clothing, or debris that indicate a house or car (and thus a person too) was swept to this location.
Given the huge demand for automating image processing, it was no surprise that a panel of four judges from the agencies awarded $900 in prizes to three students from the National Science Foundation Research Experiences for Undergraduates (REU) program. The prizes were for software that classified imagery and displayed where it was taken.
First place went to Julia Proft for an application that used computer vision and machine learning to find images from the floods which contained a spectral anomaly such as color. Below is UAV image flagged with an amaloy and anomalies indicated on a parallel image to make it easier to find where in the image the anomalies occurred without marking up the image and blocking the view.
Second place went to Matt Hegarty for an application that used computer vision and machine learning to find images from the floods which contained urban debris having straight lines or corners. In this image, the program found instances of trash, pvc poles, and other indications of where houses (and possibly victims) had been swept down river.
Third place went to Abygail McMillian for a program that visually displayed where and when UAV data or any geotagged data was taken. The spatial apsect is important for responders to see where assets have sensed- at Nepal, UAV teams often surveyed the same areas but missed important regions. The temporal aspect (a timeline scale on the map) is important because with the changes in the river flood stage, hydrological and missing person images become “stale” and flights should be reflown to get new data.
The 10 students (nine from under-represented groups) were from Colorado, Georgia, Puerto Rico, South Carolina, Texas, Vermont, and Wyoming and spent the summer conducting research through the Computing for Disasters NSF REU site grant under the direction of Dr. Robin Murphy. Multiple State agencies requested those student-created apps be hardened and released. In previous years, the panels have awarded prizes primarily to hardware- UAVs, battery systems, communications nodes, etc. This year, the focus was on software and programming to help analyze the data the hardware generates.