Semantic Segmentation
2022 - 23
Tree and River Segments, Found at Stow Lake
Oil and acrylic on canvas. 40"x36". 2023
Forest Vision Geography
Oil and acrylic on canvas. 24"x30". 2023
Tree and Palm Segments, Detected at Gill Tract Farm
Oil and acrylic on canvas. 16"x18". 2023
Tree Training
Pigment ink on archival paper. 20"x20". 2023
Landscape Machine
Pigment ink on archival paper. 20"x20". 2023
Finding Tree-like
Pigment ink on archival paper. 20"x20". 2022
Detection of Tree, Sky, Earth, Person, Hill
Pigment ink on archival paper. 20"x20". 2022
Semantic Segmentation
Pigment ink on archival paper. 20"x20". 2022
Machine Seeing Garden
Pigment ink on archival paper. 20"x20". 2022
Pre-Trained Garden Scene
Pigment ink on archival paper. 20"x20". 2023
About the series
Semantic Segmentation is a series of oil and digital paintings exploring how machines parse the visual world. Moving between physical material and computational representation, the artist translates machine-derived interpretations of landscapes back through the human hand and eye. Several works begin with a tree in Pieter Bruegel the Elder’s 1565 painting The Harvesters, transformed by computer vision into a color-coded map of semantic categories—tree, plant, sky, person—and then returned to painting.
Nature shaped the evolution of human vision and, through the images humans produce, the development of machine vision. Yet the data centers that sustain these systems increasingly consume natural resources themselves. Against the backdrop of climate change, Semantic Segmentation considers the entangled relationship between nature, human perception, and the machines we have taught to see.