SENSE: Satellite-based ENergy Synthesis for Sustainable Environment

Le résumé fourni par la source

Urban Building Energy Modeling (UBEM) 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 corresponding urban functional generation (e.g., energy layer); third, aligned high-quality high-resolution building energy data with satellite imagery is limited and scarce. To address them, 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, our framework, 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, and Busan) demonstrate that SENSE achieves high visual fidelity and strong physical consistency. Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20% labelled energy data, boosting downstream prediction performance by 10% mIoU. Compared to state-of-the-art urban building energy prediction methods, SENSE significantly reduced prediction error (reduced 3%-11% NMBE and 1%-9% CVRMSE), according to standard ASHRAE metrics. This study offers an energy-efficient urban planning and physical generation solution for urban science, energy science and building science. The dataset and code links: https://huggingface.co/datasets/skl24/MUSE and https://github.com/kailaisun/GenAI4Urban-Energy/.

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