Our Research

Core Geospatial Science

The Taylor Geospatial Institute fosters comprehensive strength in the core fields of geospatial science, addressing national gaps through a multi-faceted range of research programs and initiatives. These fields and emerging technologies are critically important to answering pressing questions in a wide variety of applied fields including agriculture, business, climate, defense, environment, and health.

Core geospatial science includes Geographic Information System (GIS), remote sensing, photogrammetry, global positioning system (GPS), Positioning, Navigation and Timing (PNT), geomatics, and geoinformatics. Researchers in these core areas build new tools to better measure Earth and advance our understanding of geospatial science. Engineers use that knowledge to develop useful technologies such as sensors, drones, robots, and satellites; and these technologies, in turn, provide data and tools that help geospatial scientists better understand the ever-changing world to address grand societal challenges.

Adjacent fields, such as geodesy, engineering, big data analytics, computer vision, artificial intelligence (AI), and imagery science, provide foundational knowledge and critical skills that make geospatial science more powerful. This information is then leveraged by disciplines for which a geospatial lens provides insight as to the causal nature of various societal and scientific challenges.

Building upon the St. Louis region’s competitive geospatial strengths, the Taylor Geospatial Institute addresses grand societal challenges in food systems, health systems, and national security with cutting-edge GeoAI and data analytics techniques. We support research to improve food security, foster health and social equity, and build smarter cities and resilient communities. This in turn promotes economic development by translating research into commercial applications and training the next generation of the workforce.

The Geospatial Frontier

Rapid advances in sensor technology, field robotics, unmanned aerial systems (UAS), and computing power have facilitated exponential growth in the application of geospatial technology. Meanwhile, processing complex, multiscale, and multidimensional data from UAS, satellites, environmental sensors, and climate model simulations has become increasingly difficult for both scientists and the public. Handling big datasets is essential to efficiently summarize, analyze, and visualize the abundant information gathered for agricultural, environmental, social and health assessments with direct applications to education, training and decision-making.

There exist numerous opportunities for broader usage of multi-sensor, remote sensing data, and advanced algorithms for translating the abundant spectral and spatial data to generate and extract information useful for decision-making. The TGI is investing in advanced data collection facilities including ground, airborne, and satellite sensors, as well as people with skills in GeoAI, cloud computing, and big data analytics to address these challenges.

We are also investing in developments of new sensors, such as acoustic sensors and micro satellites, that are critical for finding solutions to the mounting challenges of climate change and assessing the impact of the oil and gas industry on the environment. These new sensors and technology will have a big impact on how we monitor and track greenhouse gas emissions and mitigate their impact.

Geospatial Health

Geospatial health research measures and analyzes the complex building blocks of health the connect all of humanity. Using geospatial technology to study air quality and water access enhances our understanding of the health impacts of where we live, work, and play. 

It also has important applications for COVID-19 risk assessment, infectious disease prediction modeling, e‑health tools such as telehealth and sensor-based diagnostics, and disaster preparedness. For example, geospatial health researchers can combine public health and healthcare data from disparate sources to inform an early sensing system that would provide real-time COVID-19 risk assessments. The data sources could include measures of community mobility such as:

• App-based symptom tracking and contact tracing
• Anonymized smartphone data
• Geolocated social media mentions
• Satellite imagery analyses to identify vehicle traffic patterns in health care locations
• Geolocated search terms

Synchronizing these data sources and fusing them to follow real-time models can provide critical insights into community-level COVID-19 risks.