Research
Geospatial Computational Intelligence and Computer Vision
Geospatial AI Locates Masaailand Bomas for Non-Profit To Deliver Healthcare
Lead: Keli Cheng, PhD Candidate
We explore the Maasailand of Tanzania, to evaluate the use of deep neural networks (DNN) to aid in the automatic visual analysis of remote sensing data to geo-locate Maasai boma structures.
Read More:
Mizzou Engineers Help Locate Remote Bomas in East Africa with Geo-AI
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Collaborators:
Lincoln Sheets, formerly with Health Management & Informatics
Ilinca Popescu, Stanford University
Machine Learning Enabled Hazard Tracking with Low-Altitude UAS & Vehicle-borne Sensors
Team: Trevor Bajkowski, David Huangal, Alex Hurt, and Jeff Dale
This research involves the tracking of objects of interest across space and time from a UAS video feed and vehicle-borne sensors. We are creating a 3-dimensional situational awareness to enable safe navigation in hazardous areas, such as result from natural disasters. Future systems will enable dynamic vehicle autonomy in complex environments.
Geospatial AI for Modeling Environmental and Climate Fitness for Human Health
details coming soon
Applied Machine Learning and Advanced Data Analytics for Eldercare
details coming soon