Disaster Imaging Inc
Observe. Innovate. Understand.
Pioneering the integration of UAS, satellite, radar, modeling and AI to transform how we understand and respond to tornadoes
Our Mission
Our mission is to reveal how tornadoes behave and cause damage by integrating cutting-edge observations with advanced modeling and AI— strengthening both science and community resilience.
DII collects comprehensive observations of tornadoes and their impacts using Uncrewed Aircraft Systems (UAS), satellites, ground-based cameras, and field measurements. By integrating these data with model simulations and artificial intelligence, we examine new insights into tornado behavior and the damage they produce.
We bridge the gap between innovative research and real-world impact – advancing fundamental science while serving communities, especially in rural areas that traditionally have not been a research focus.
How DII Transforms Tornado Research
Disaster Imaging Inc (DII) uniquely combines expertise from several scientific specialties to advance our understanding of tornadoes and the damage they produce…
Quick Response Observations
Years of experience have taught us how to pre-position assets, adapt to the disaster and the field conditions, and obtain unprecedented data sets. DII collects observations of tornadoes and their impacts using Uncrewed Aircraft Systems (UAS), satellites, ground cameras and in-situ observations. We use AI to quickly assess the nature and severity of the event.
Computer modeling
We will perform innovative modeling to understand the winds, damage, and debris at the ground where lives are impacted. Our high-resolution simulations reveal tornado behavior at scales never before possible – from the vortex structure to individual debris trajectories.
Artificial Intelligence
We are developing unique tools to understand and predict tornado damage patterns, intensity, and impacts by combining theory, modeling, and high-resolution damage imagery and analyses. Our machine learning approaches extract patterns from massive datasets that would be impossible to identify manually.