Situated in an era of abundant access to digital images and imminent environmental collapse, Landscape Past Future proposes a re-thinking of landscape paintings through the lens of data aggregation technologies.

Using large institutional datasets filtered via metadata search terms and machine-learning techniques, new landscape paintings are created by aggregating historically significant works in digital mosaics. In these new, original works, the underlying pastoral associations are reconstituted within a digital realm of preservation: a virtual simulacrum in dialogue with our present witnessing of a disappearing natural world.

Each digital mosaic is presented with an accompanying “works cited” page, detailing the sources used to create the composite image. The accounting for every borrowed pixel functions as a “general recipe” and list of references, while further complicating the underlying tension between quantitative procedures and qualitative choices which lies at the heart of the project.

Photo credit (book spreads) : Guy L’Heureux, courtesy of Adam Basanta / ELLEPHANT