Brendan Brown

After the Flight: Leveraging Machine Learning to Make Sense of Drone Data

Brendan Brown, Environmental Scientist, CDM Smith

Drone technology has transformed how environmental and infrastructure data are collected, but for many practitioners, the real challenge begins after the flight. Gigabytes of data from high-resolution cameras, lidar, and multispectral sensors represent a hurdle to driving data to decisions. By integrated machine learning and deep learning into the process, we can pull out actionable intelligence upon with human subject matter experts can base their decisions.  This presentation will showcase how machine learning and deep learning models are being applied to classify land cover, map invasive species, detect changes, and predict ecological impacts with unprecedented accuracy. These tools not only improve decision-making but also streamline regulatory compliance and permitting workflows. Attendees will gain insights into practical applications, implementation strategies, and future directions for AI and geospatial technologies in Florida’s environmental sector. 

Brendan V. Brown is an environmental scientist with over 20 years of experience supporting complex environmental and infrastructure projects across the United States. He specializes in wetland delineation, permitting, ecological restoration, and integrating Nature-Based Solutions into project delivery. For more than 8 years, Brendan has been at the forefront of applying machine learning and drone technology to environmental challenges, developing workflows that transform high-resolution imagery into actionable insights. His work focuses on using deep learning to accelerate field investigations, improve data consistency, and support defensible, regulator-ready outcomes. As a technical leader, Brendan has helped advance innovative tools that reduce field time, streamline permitting, and enhance decision-making for transportation, civil works, and remediation projects. He regularly collaborates with multidisciplinary teams to bridge the gap between emerging technology and practical implementation in the field.