mlsanityData to 7 Oct 2026

HeatPrompt: Zero-Shot Vision-Language Modeling of Urban Heat Demand from Satellite Images

arXiv 2602.20066 ↗First posted 23 Feb 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Accurate heat-demand maps play a crucial role in decarbonizing space heating, yet most municipalities lack detailed building-level data needed to calculate them. We introduce HeatPrompt, a zero-shot vision-language energy modeling framework that estimates annual heat demand using semantic features extracted from satellite images, basic Geographic Information System (GIS), and building-level features. We feed pretrained Large Vision Language Models (VLMs) with a domain-specific prompt to act as an energy planner and extract the visual attributes such as roof age, building density, etc, from the RGB satellite image that correspond to the thermal load. A Multi-Layer Perceptron (MLP) regressor trained on these captions shows an R2 uplift of 93.7% and shrinks the mean absolute error (MAE) by 30% compared to the baseline model. Qualitative analysis shows that high-impact tokens align with high-demand zones, offering lightweight support for heat planning in data-scarce regions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 9 of its 37 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

9 of 37 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering3 Nov
  2. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensing19 Jun

Other references · 7

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2025Qwen2.5-VL Technical Reportcited by 5,418
  6. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 255
  7. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 166

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  3. 2025Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
  4. 2026HeatMat: Simulation of City Material Impact on Urban Heat Island Effect
  5. 2026CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training
  6. 2024Towards Zero-Shot Annotation of the Built Environment with Vision-Language Models (Vision Paper)
  7. 2024Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
  8. 2026Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models