Publication: CLIP and the City: Addressing the Artificial Encoding of Cities in Multimodal Foundation Deep Learning Models
CLIP and the City: Addressing the Artificial Encoding of Cities in Multimodal Foundation Deep Learning Models
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Negueruela del Castillo, D., & Neri, I. (2023). CLIP and the City: Addressing the Artificial Encoding of Cities in Multimodal Foundation Deep Learning Models (R. Bogdanović, Ed.; pp. 100–109). STRAND – Sustainable Urban Society Association. https://www.strand.rs/2023-proceedings/
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In this project, we propose and explore a computational pipeline to examine urban cultural landscapes through the lens of artificial intelligence, and for questioning modes of embedding culture in machine learning models. By employing machine learning models that extract features and textual properties from images, we aim to uncover the connections between a city's history, architecture, and urban development. The city of Rome serves as a significant case study for this research. To achieve this objective, we feed 360° panoramic image
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Negueruela del Castillo, D., & Neri, I. (2023). CLIP and the City: Addressing the Artificial Encoding of Cities in Multimodal Foundation Deep Learning Models (R. Bogdanović, Ed.; pp. 100–109). STRAND – Sustainable Urban Society Association. https://www.strand.rs/2023-proceedings/
