‘ioView is a computer vision AI for rapid damage assessment named after the native Hawaiian Hawk

‘ioView (‘io, pronounced “ee-oh”) is an end-to-end disaster assessment platform that augments the capabilities of recovery crews in the wake of manmade and natural disasters. After a disaster, every minute counts and each moment without vitals services, such as power puts the health and safety of the hardest hit more at risk. ‘ioView was developed under a project with the U.S. Department of Energy.

‘ioView processes imagery and/or video data taken after disasters and identifies damaged versus intact infrastructure quickly, over large areas. That data assessment can then used to position or redirect recovery personnel, equipment, and other resources to the most critical areas, saving time, effort, and potentially lives.

Areas that lose access to the power in the wake of disasters suffer significantly larger and longer-lasting issues. Hurricanes, earthquakes, tsunamis, and other disasters affect wide swathes of land, but the areas that lose power are set back much more than areas that were able to retain power. In a typical post-disaster scenario, evaluation crews are sent out to manually inspect power lines and pole infrastructure, literally navigating from pole to pole and conducting visual assessments. This is time-consuming, can be hazardous, and involves multiple personnel per crew to complete. ‘ioView helps these crews by quickly identifying and classifying infrastructure that is no longer intact and rating the ‘health’ of that infrastructure. The location of the damage is also logged and mapped against GIS information.

Input data can be taken from (but not limited to) UAV drone footage, fixed-wing video, dash cam video, and hand-held or mobile phone imagery. That footage is analyzed by Machine Learning algorithms that have been trained to identify objects such as downed electricity poles. ‘ioView assesses if those objects are within their standard operating parameters or not. Input data is tagged with location information, allowing recovery crews to pinpoint specific locations of infrastructure damage over a large range of affected area.

The ‘ioView can automates the labor-intensive, time-consuming manual evaluation methods allowing for crews to focus in on hardest-hit areas. The platform is flexible and allows for human interaction, cross checking of data, and manual/mobile app data entry – all in the effort to augment the capabilities of recovery crews and those directing resources.

With ‘ioView acting in real-time support, human crews can much more efficiently allocate resources and position personnel, getting communities on the road to recovery sooner. Built upon element of Oceanit’s award winning MERCI damage assessment platform, ‘ioView uses an intuitive and standardized framework modeled after FEMA’s damage assessment process.