Researchers have developed SENSE, a novel generative framework for urban building energy modeling that synthesizes realistic satellite imagery along with aligned building energy consumption and height maps. This controllable diffusion model leverages large vision models to generate urban functional data, addressing limitations in existing predictive approaches and the scarcity of aligned, high-resolution building energy datasets. SENSE can generate sufficient annotated synthetic data using a small fraction of labeled energy data, significantly boosting downstream prediction performance and reducing prediction errors compared to state-of-the-art methods. AI
影响 Enables more efficient urban planning and physical generation by creating synthetic energy data, potentially accelerating sustainable development goals.
排序理由 The cluster contains an academic paper detailing a new methodology and framework for AI-driven urban energy modeling. [lever_c_demoted from research: ic=1 ai=1.0]
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