The leaf architectures of the Damaged Leaf Dataset (DLD) are ruins and creations. Collected in Colchester, VT, during the Spongy Moth outbreaks of 2021 and 2022, they are visual documents of conflict between human, animal, and plant behaviors. In this project, I play the role of artist/cryptographer, first collecting the leaves and photographing them, then displaying them in tables and diagrams, and lastly, deciphering their messages. My decryption process incorporates local knowledge and conversation with local people, algorithms, and artificial intelligence.
Do you see monsters? Do you see ancient or mythological creatures? Do you see memories? Do you see messages? What is this pattern language telling you?
DLD Generation I
Damaged by Spongy Moth caterpillar consumption, these intricately patterned leaves fall from trees in spring and autumn.
DLD Generation II
During a Spongy Moth outbreak, there is dramatic defoliation in the first months of spring. The resilient trees grow a second set of leaves, smaller than the first foliage; their forms are asymmetrical and twisted.
DLD Generation III
The leaves of Generation III are generated by a machine-learning model. This model is trained on DLD Gen I and Gen II. Through the consumption of this dataset, the model learns a visual language, a language of an environmental crisis, a language that is as much flora as it is fauna, as it is human and machine.