HeatMat: Simulation of City Material Impact on Urban Heat Island Effect
- URL: http://arxiv.org/abs/2601.22796v1
- Date: Fri, 30 Jan 2026 10:20:47 GMT
- Title: HeatMat: Simulation of City Material Impact on Urban Heat Island Effect
- Authors: Marie Reinbigler, Romain Rouffet, Peter Naylor, Mikolaj Czerkawski, Nikolaos Dionelis, Elisabeth Brunet, Catalin Fetita, Rosalie Martin,
- Abstract summary: The Urban Heat Island (UHI) effect is a significant increase in temperature in urban environments compared to surrounding areas.<n>Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas.<n>We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city.
- Score: 5.9791504486574425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resolution. Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas. To analyze their individual impact and to test new material configurations, a high-resolution simulation at the city scale is required. Estimating the current materials used in a city, including those on building facades, is also challenging. We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city, relying only on open data. We estimate building materials using street-view images and a pre-trained vision-language model (VLM) to supplement existing OpenStreetMap data, which describes the 2D geometry and features of buildings. We further encode this information into a set of 2D maps that represent the city's vertical structure and material characteristics. These maps serve as inputs for our 2.5D simulator, which models coupled heat transfers and enables random-access surface temperature estimation at multiple resolutions, reaching an x20 speedup compared to an equivalent simulation in 3D.
Related papers
- 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) - Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes [0.9208007322096533]
This paper introduces SAGAI: Streetscape Analysis with Generative Artificial Intelligence.<n>It is a modular workflow for scoring street-level urban scenes using open-access data and vision-language models.<n>It operates without task-specific training or proprietary software dependencies.
arXiv Detail & Related papers (2025-04-23T09:08:06Z) - Predicting Air Temperature from Volumetric Urban Morphology with Machine Learning [0.0]
We introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities.<n>Those voxelized 3D city data from multiple cities and corresponding air temperature data are used to develop a machine learning model.
arXiv Detail & Related papers (2025-01-16T11:10:38Z) - Urban Air Temperature Prediction using Conditional Diffusion Models [26.577558935382477]
Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect.<n>Air temperature at 2m above the surface is a key indicator of the UHI effect.<n>How land use land cover (LULC) affects $T_a$ is a critical research question which requires high-resolution (HR) $T_a$ data at neighborhood scale.<n>We propose a novel method to predict HR $T_a$ at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from
arXiv Detail & Related papers (2024-12-18T04:56:29Z) - Urban Scene Diffusion through Semantic Occupancy Map [49.20779809250597]
UrbanDiffusion is a 3D diffusion model conditioned on a Bird's-Eye View (BEV) map.
Our model learns the data distribution of scene-level structures within a latent space.
After training on real-world driving datasets, our model can generate a wide range of diverse urban scenes.
arXiv Detail & Related papers (2024-03-18T11:54:35Z) - Semantic segmentation of longitudinal thermal images for identification
of hot and cool spots in urban areas [1.124958340749622]
This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas.
A subset of the thermal image dataset was used to train state-of-the-art deep learning models to segment various urban features.
arXiv Detail & Related papers (2023-10-06T13:41:39Z) - MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering
and Beyond [69.37319723095746]
We build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches.
We develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities.
The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km2$.
arXiv Detail & Related papers (2023-09-28T16:06:02Z) - 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) - Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof
Structures from Point Clouds [4.38301148531795]
Existing datasets for 3D modeling mainly focus on common objects such as furniture or cars.
We present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2.
Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises.
arXiv Detail & Related papers (2023-07-21T21:38:57Z) - SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point
Clouds [52.624157840253204]
We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2.
Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
arXiv Detail & Related papers (2022-01-12T14:48:11Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z)
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.