SENSE: Satellite-based ENergy Synthesis for Sustainable Environment

• URL: http://arxiv.org/abs/2605.18101v1
• Date: Mon, 18 May 2026 09:13:01 GMT
• Title: SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
• Abstract summary: Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11.<n>Here we propose SENSE, a unified generative UBEM framework that jointly synthesizes realistic urban satellite imagery and aligned high-quality building energy consumption and height maps.<n> Experiments demonstrate that SENSE can generate enough annotated synthetic data using less than 20% labeled energy data, boosting downstream prediction performance by 10% IoU.
• Score: 15.843163909588851
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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/.

Related papers

• CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training [41.99844472131922]
  CarbonCLIP is a task-oriented multimodal distillation framework that improves satellite-based carbon emission prediction.<n>Unlike conventional methods that rely on static visual features, CarbonCLIP explicitly bridges the gap between top-down satellite views and ground-level human activities.<n>CarbonCLIP requires multimodal data only during the pretraining phase; during inference, it relies solely on satellite imagery.
  arXiv  Detail & Related papers  (2026-07-08T11:33:35Z)
• Beyond Resilience -- A Conceptual Framework for Civic Ascent [45.88028371034407]
  Civic ascent is defined as the condition in which a city emerges from shock with higher functional capacity than before.<n>Post-shock ascent is measured by a normalised improvement index A(T) applied to a composite civic performance signal P(t)
  arXiv  Detail & Related papers  (2026-06-10T23:38:43Z)
• Envisioning global urban development with satellite imagery and generative AI [15.552739333456172]
  This study designs a multimodal generative AI framework to envision sustainable urban development at a global scale.<n>By integrating prompts and geospatial controls, our framework can generate high-fidelity, diverse, and realistic urban satellite imagery.<n>It also facilitates urban redevelopment practices by learning from the surrounding environment.
  arXiv  Detail & Related papers  (2026-03-27T05:07:45Z)
• LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions [80.70675855203154]
  Inverse rendering in urban scenes is pivotal for applications like autonomous driving and digital twins.<n>Yet, it faces significant challenges due to complex illumination conditions, including multi-illumination and indirect light and shadow effects.<n>We present LightCity, a novel high-quality synthetic urban dataset featuring diverse illumination conditions with realistic indirect light and shadow effects.
  arXiv  Detail & Related papers  (2026-02-01T09:37:00Z)
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response [50.76124284445902]
  Building damage assessment (BDA) is an essential capability in the aftermath of a disaster to reduce human casualties.<n>Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events.<n>We present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
  arXiv  Detail & Related papers  (2025-01-10T14:57:18Z)
• Adopting Explainable-AI to investigate the impact of urban morphology design on energy and environmental performance in dry-arid climates [0.0]
  This study advances urban morphology evaluation by combining Urban Building Energy Modeling (UBEM) with machine learning methods (ML) and Explainable AI techniques.<n>Using Tehran's dense urban landscape as a case study, this research assesses and ranks the impact of 30 morphology parameters on key energy metrics.<n>Findings reveal that building shape, window-to-wall ratio, and commercial ratio are the most critical parameters affecting energy efficiency.
  arXiv  Detail & Related papers  (2024-12-13T09:19:49Z)
• AerialGo: Walking-through City View Generation from Aerial Perspectives [48.53976414257845]
  AerialGo is a framework that generates realistic walking-through city views from aerial images.<n>By conditioning ground-view synthesis on accessible aerial data, AerialGo bypasses the privacy risks inherent in ground-level imagery.<n>Experiments show that AerialGo significantly enhances ground-level realism and structural coherence.
  arXiv  Detail & Related papers  (2024-11-29T08:14:07Z)
• Solar potential analysis over Indian cities using high-resolution satellite imagery and DEM [0.0]
  We have implemented a novel approach to estimate rooftop solar potential using inputs of high-resolution satellite imagery (0.5 cm), a digital elevation model (1m), along with ground station radiation data. It was observed that due to seasonal variations, environmental effects and technical reasons such as solar panel structure etc., there can be a significant loss of electricity generation up to 50%.
  arXiv  Detail & Related papers  (2024-11-07T10:50:39Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials [6.573006589628846]
  Most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data. We propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials.
  arXiv  Detail & Related papers  (2022-01-06T21:58:37Z)
• Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network [20.358180125750046]
  Building industry accounts for more than 40% of energy consumption in the United States. UBEM is the foundation to support the design of energy-efficient communities. Data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
  arXiv  Detail & Related papers  (2021-05-27T19:02:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.