SOURCE-LINKED INTELLIGENCE

Time-Traveling Trees: Machine-reading the Past, Present, and Future of Climate Change Adaptation from Archival Aerial Photography

CORDIS · observation · Publication date unknown

esent, and Future of Climate Change Adaptation from Archival Aerial Photography This project begins with a “book:” eight historical case studies detailing environmental change will serve to produce 8 machine learning training data sets to develop a methodology for the automatic analysis of quantitative and visual environmental data from historical aerial photographs. This will allow for a new approach to study the 1940s-1990s Great Acceleration, which caused unprecedented environmental change. Becoming ubiquitous in the 1940s, aerial photography has a sub-1-meter resolution which is key to identifying non-linear, non-monumental, and pre-industrial rural environmental infrastructure (e.g. trees, waterholes, fields) and how it has changed over time. Since satellite imagery only gained a comparable high resolution in the early 2000s, the ability to machine-read aerial photographs will quadruple the availability of high-resolution geospatial data, thereby allowing a more robust and nuanced analysis of environmental and climate change. The project goals are: (1) to develop a historical gr

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

2499750

unit

EUR