# Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling

arXiv: https://arxiv.org/abs/2404.07435 · HTML: https://mlsanity.com/paper/2404.07435/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [Generative and Discriminative Voxel Modeling with Convolutional Neural Networks](https://mlsanity.com/paper/1608.04236/) (2016)
- [Adversarial Generation of Continuous Implicit Shape Representations](https://mlsanity.com/paper/2002.00349/) (2020)
- [Urban Building Energy Modeling (UBEM) Tools: A State-of-the-Art Review of bottom-up physics-based approaches](https://mlsanity.com/paper/2103.01761/) (2021)

## Built on by (most cited first, 1 of 1)

- [MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture](https://mlsanity.com/paper/2511.11587/) (2025)

## Abstract (arXiv)

As the global population and urbanization expand, the building sector has emerged as the predominant energy consumer and carbon emission contributor. The need for innovative Urban Building Energy Modeling grows, yet existing building archetypes often fail to capture the unique attributes of local buildings and the nuanced distinctions between different cities, jeopardizing the precision of energy modeling. This paper presents an alternative tool employing self-supervised learning to distill complex geometric data into representative, locale-specific archetypes. This study attempts to foster a new paradigm of interaction with built environments, incorporating local parameters to conduct bespoke energy simulations at the community level. The catered archetypes can augment the precision and applicability of energy consumption modeling at different scales across diverse building inventories. This tool provides a potential solution that encourages the exploration of emerging local ecologies. By integrating building envelope characteristics and cultural granularity into the building archetype generation process, we seek a future where architecture and urban design are intricately interwoven with the energy sector in shaping our built environments.