SENSE：基于卫星的可持续环境能源综合

摘要

城市建筑能耗建模在实现联合国可持续发展目标7和11中发挥着关键作用。尽管基于卫星影像和深度学习的现有研究已取得显著进展，但仍面临诸多挑战：多数研究本质上是预测性的，未能体现城市规划的生成式特性；虽然生成式人工智能和扩散模型在卫星影像领域呈现爆发式发展，但缺乏城市功能生成（如能源层）；此外，与卫星影像对齐的高质量高分辨率建筑能耗数据极为稀缺。为此，我们提出SENSE（面向可持续环境的卫星能源合成），这是一个统一的生成式UBEM框架，能够联合合成真实城市卫星影像以及与影像对齐的高质量建筑能耗地图和高度地图。SENSE以道路网络和城市密度指标为条件，基于可控扩散模型，利用大型视觉模型所学知识在潜在空间中生成城市建筑能耗和高程信息（标注数据）。在四个城市（纽约、波士顿、里昂、釜山）的实验表明，SENSE实现了高视觉保真度和强物理一致性，满足ASHRAE标准指标。实验同时证明，SENSE仅使用不到20%标注能耗数据即可生成充足的合成标注数据，将下游预测性能提升10% IoU。与最先进的城市能耗预测方法相比，SENSE显著降低了预测误差（NMBE降低3%-11%，CVRMSE降低1%-9%）。本研究为城市科学、能源科学和建筑科学提供了一种能效导向的城市规划和物理生成解决方案。数据集与代码：https://huggingface.co/datasets/skl24/MUSE 和 https://github.com/kailaisun/GenAI4Urban-Energy/。

English

Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery and deep learning have achieved remarkable progress, many challenges exist: most existing studies are inherently predictive, failing to reflect the generative nature of urban planning; although generative AI and diffusion models have seen explosive growth in satellite imagery, they lack the urban functional generation (e.g., energy layer); third, aligned high-quality high-resolution building energy data with satellite imagery is limited and scarce. Here we propose SENSE (Satellite-based ENergy Synthesis for Sustainable Environment), a unified generative UBEM framework that jointly synthesizes realistic urban satellite imagery and aligned high-quality building energy consumption and height maps. By conditioning on road networks and urban density metrics, SENSE, based on a controllable diffusion model, leverages the knowledge learned by large vision models to generate urban building energy consumption and height information (annotations) in the latent space. Experiments across four cities (New York City, Boston, Lyon, Busan) demonstrate that SENSE achieves high visual fidelity and strong physical consistency, satisfying the ASHRAE standard metric. Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20% labeled energy data, boosting downstream prediction performance by 10% IoU. Compared to SOTA urban energy prediction methods, SENSE significantly reduced prediction error (reduced 3%-11% NMBE and 1%-9% CVRMSE). This study offers an energy-efficiency urban planning and physical generation solution for urban science, energy science and building science. The dataset and code: https://huggingface.co/datasets/skl24/MUSE and https://github.com/kailaisun/GenAI4Urban-Energy/.