Project in Process: Machine Learning in the CRS Architectural Archive
Abstract
Architectural histories of the recent past are challenged by the overwhelming quantity and complexity of documentation. This is especially the case for histories of large professional practices. Researchers at Texas A&M University have addressed some of these challenges by introducing machine learning-based data practices into the processing of the CRS Archives, which holds, among other things, the largest collection of historic architectural programs from the second half of the twentieth century.