Measuring the Anthropocene: Machine-Reading Historical Aerial Photographs to Assess Long-Term Biodiversity Loss During the Great Acceleration

2026 Faculty Research Award

AWARD PERIOD: 2026-2028

Starting in the 1940’s, biodiversity loss accelerated due to industrialization, population growth, and agricultural expansion. To help map these changes, PIs, Emmanuel Krieke, Norman B. Tomlinson ’16 and ’48 Professor of War and Society and Professor of History, Xiaoyan Li, Senior Lecturer of Computer Science, Tsering Wangyal Shawa, Geographic Information Systems and Map Librarian, and Bill Guthe, Senior Geographical Information Systems Visualization Analyst, will train machine learning systems to extract vegetation and land use information from archived aerial photographs. Most climate and biodiversity models rely on vegetation data derived from satellite imagery, but satellite images collected before the 2000’s were too low resolution for this purpose. Aerial photographs captured between the 1940’s and 1990’s could be leveraged to understand how land use and biodiversity changed over that period. However, current machine learning systems are unable to read aerial photographs, and extracting this data manually is tedious and time consuming. To enable data to be efficiently extracted from aerial photographs, the team will collect and process aerial photographs from several case study areas, including regions of Namibia, South Africa, Nigeria, Colombia, Cuba, Sri Lanka, and Vietnam. They will then use these case study photographs to train machine learning systems to identify and extract data from aerial photographs. By unlocking the potential of the tens of millions of aerial photographs stored in archives and depositories around the world, this project will empower researchers to more accurately assess the extent of biodiversity loss during the 20th century and predict future changes.

 

Participating Departments

 

Faculty

Emmanuel Krieke
Norman B. Tomlinson ’16 and ’48 Professor of War and Society; Professor of History
Xiaoyan Li
Senior Lecturer of Computer Science

 

Additional Researchers