Buildings as Text: A Universal Regression Paradigm for Building Attribute Prediction

Predicting building attributes—such as functional classification, socioeconomic status, and energy efficiency—is a fundamental task in urban science. The current paradigm involves leveraging domain knowledge to extract attribute-specific morphological or topological features for supervised modeling. However, this heavy reliance on manual feature engineering often leads to task-specific models where features must be redefined for each attribute. Consequently, the field lacks a unified, generalizable framework capable of multi-attribute building prediction.

Inspired by recent advances in Regression Language Models (RLMs), which cast continuous prediction as a text-to-text task, we propose Buildings as Text (BaT). BaT serializes structured building representations (e.g., GeoJSON) into raw text and enables end-to-end text-to-text regression. To mitigate the spatial sensitivity of building data, we introduce a Topology-Preserved Coordinate (TPC) strategy that removes each building text’s absolute positional information. Specifically, TPC applies a global coordinate shift to the serialized geometry, suppressing absolute-location bias while preserving local shape and topology. By operating directly on raw text, BaT eliminates manual feature engineering and allows the model to learn a “spatial syntax” from the underlying geometric descriptions.

We validated the BaT framework through a case study on informal settlement (slum) classification. The results demonstrate that our model achieves superior performance and higher adaptability compared to traditional morphology-based methods. While validated on slum detection, this research offers a universal and scalable paradigm for urban building analysis, suggesting that Large Language Models can effectively "read" urban forms for diverse prediction tasks beyond specific domains.