Integrating Artificial Intelligence and Thermal Imagery to Streamline Wildlife Monitoring

Student Researchers

Andrew Lee '25, Major: Data Science (2024)

Emilio Luz-Ricca ‘23, Major: Data Science; Minor:  Economics (2021-2023)

Faculty Mentors

Dr. Robert Rose and Dr. Gregory Hunt

Project Description

Streamlining and automating systems for monitoring wildlife populations is critical for informing and evaluating conservation management and policy. Given the rapid changes in climate, land development, and wildlife habitat, there is a need to streamline wildlife population monitoring to guide and monitor conservation action and management. The combination of very high-resolution aerial remote sensing and deep learning techniques has the potential to provide an automated, efficient means to achieve these survey goals.

As part of an ongoing collaboration, W&M students are working with the U.S. Fish and Wildlife Service (USFWS) Division of Migratory Bird Management and partners to develop a streamlined methodology that utilizes deep learning to identify sandhill cranes from thermal imagery to improve the efficiency and effectiveness of USFWS monitoring and management strategy. In 2022, Emilio Luz Ricca'23 developed the first deep learning model that explored scale and other factors needed to ensure the model was effective in meeting partners' needs. Building upon this research, Andrew Lee '25 scaled the models and built a framework for ecologists to solve challenges with their implementation, such as the detection of non-target species and dealing with large datasets. This ongoing student-led research project is supporting USFWS and partners with improved efficacy of their sandhill crane monitoring efforts and will improve our understanding of the applications of deep learning approaches in conservation more broadly.

Project ID - Format

21-012-21- CRP Year

21-012-23 - CRP Semester

21-012-24 - CRP Year