Urban Heat Island Mitigation

Derniers papiers

• A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation

  Hind Yousif Alhammadi, Isam Mashhour Al Jawarneh · 6 octobre 2026

  This paper presents the workflow for building an automated ArcGIS Pro tool using ArcPy to extract the Land Surface Temperature (LST) from Landsat 7, 8 and 9 datasets. The tool eliminates the need for manual band selection and repetitive raster computations by automating the multi-step workflow of ra…

• UHI-Bench: Benchmarking Dual-Source Urban Heat Island Modeling Across Cities in Diverse Climate Regimes

  Wanyun Ling, Chenxi Liu, Yi Xie, Aopu Xu, Zhuoqi Zeng, Ziyue Li · 26 août 2026

  Urban heat islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks. Their two primary observations, land surface temperature UHI (LST-UHI) and near-surface air temperature UHI (AirT-UHI), capture physically distinct aspects of urban heat. However, most studies rely …

• Evaluating Neural Cartographic Relief Shading for Urban Environments: A Downtown Calgary Study Using High-Resolution DEM and DSM Data

  Emmanuel Stefanakis · 21 août 2026

  This article explores the performance of analytical and neural-based hillshading methods in a dense urban environment using high-resolution digital elevation model (DEM) and digital surface model (DSM) data for downtown Calgary. The study compares single-direction and multi-direction analytical hill…

• From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California

  Mohammadreza Narimani, Shreyan Mitra, Parastoo Farajpoor · 17 août 2026

  Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optical GeoAI workflow for Davis, California, using 2022 National Agriculture Imagery Program imagery (0.6 m RGB+NIR). DeepForest generated crown candidates; an NDV…

• HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities

  Jesus Guerrero, Isaac Corley, Leon Najafirad, Maryam Tabar, Paul Rad · 10 août 2026

  Land Surface Temperature (LST) is a widely used satellite-derived measure of urban surface heat, but there is no shared benchmark for forecasting it at 30 m. Prior studies usually cover one to three cities, use kilometer-scale products, or do not release data and code. We introduce HeatCast, a Lands…

• Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data

  Htet Yamin Ko Ko · 27 juillet 2026

  The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such information underpins the assessment of ecosystem services provided by green infrastructure, particularly for urban heat island (UHI) mitigation. Although…

• Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction

  Marwa Alfouly, Smajil Halilovic, Nils Bochow, Thomas Hamacher, Niklas Boers, Konrad Schindler · 23 juillet 2026

  Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data gaps due to the presence of clouds. As LST is essential for understanding land-atmosphere interactions, numerous methods…

• Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

  Htet Yamin Ko Ko, Clement Atzberger · 19 juin 2026

  Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this. However, many cities still use coarse ~100-m resolution LCZ records, which are unsuitable for fine-scale…

• Urban Heat MiniCubes: An AI-Ready dataset for urban heat research

  Jonathan Starfeldt, Maria J. Molina, Alexander Kerr, Adam Yang, Thomas R. H. Holmes, Christopher R. Hain · 11 juin 2026

  Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales. We present "Urban Heat Mi…

• A Mechanism-Coupled Split Window Network for Medium- to High-Resolution Land Surface Temperature Retrieval

  Tian Xie, Menghui Jiang, Chao Zeng, Huifang Li, Guanhao Zhang, Chan Li, Huanfeng Shen · 8 juin 2026

  Land surface temperature (LST) is a fundamental physical variable in land-atmosphere interactions, surface energy budgets, and climate processes. LST derived from medium- to high-resolution thermal infrared (TIR) observations effectively reveals thermal environmental disparities across distinct land…

• TeX-1500: A Paired Real-World LWIR Hyperspectral Dataset and Benchmark for Temperature-Emissivity-Texture Decomposition

  Cheng Dai, Jiale Lin, Hongyi Xu, Bingxuan Song, Ziyang Xie, Fanglin Bao · 3 juin 2026

  Temperature-emissivity-texture (TeX) decomposition seeks to recover object heat state, material spectral response, and visible-like geometric texture from long-wave infrared hyperspectral imaging (LWIR HSI). Existing TeX pipelines are mainly scene-specific inverse solvers, and the lack of paired LWI…

• Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints

  Reda Snaiki, Abdelatif Merabtine · 2 juin 2026

  Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis, but practical deployments are limited by sensor budgets and spacing constraints. This study proposes an uncertainty-aware graph neural network (GNN)…

• ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society

  Longchao Da, Mithun Shivakoti, Xiangrui Liu, T Pranav Kutralingam, Yezhou Yang, Hua Wei · 21 mai 2026

  Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine-grained shade patterns, especially those induced by urban buildings, strongly influence pedestrians' thermal exposure and outdoor activity planning. However, accurately modeling …

• SENSE: Satellite-based ENergy Synthesis for Sustainable Environment

  Kailai Sun, Mingyi He, Heye Huang, Can Rong, Alok Prakash, Baoshen Guo, Shenhao Wang, Jinhua Zhao · 19 mai 2026

  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 predicti…

• GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment

  Adis Alihod\v{z}i\'c · 19 mai 2026

  Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk assessment. The study was carried out in Sarajevo by using MODIS lan…

• Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

  Solomiia Kurchaba, Angela Meyer · 14 mai 2026

  Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LST products provide either high spatial or high temporal resolution, resulting in a fundamental trade-off between the two. To address this trade-off,…

• Beyond Land Surface Temperature: Explainable Spatial Machine Learning Reveals Urban Morphology Effects on Human-Centric Heat Stress

  Yuan Wang, Shengao Yi, Xiaojiang Li, Pengyuan Liu, Zhiwei Yang, Ronita Bardhan, Rudi Stouffs · 27 avril 2026

  Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spatial heterogeneity of heat exposure and its drivers is vital for climate-adaptive urban planning. However, most planning-oriented studies rely on la…

• When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution

  Yiheng Chen, Zihui Ma, Peishi Jiang, Yilong Dai, Qikai Hu, Xinyue Ye, Lingyao Li, Rita Sousa, Runlong Yu · 21 avril 2026

  Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observations severely underdetermine fine-scale structure. In this paper, we propose Earth Foundation Model-guided Diffusion (EFDiff), a novel framework for su…

• Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns

  Baris Sarper Tezcan, Hrishikesh Viswanath, Rubab Saher, Daniel Aliaga · 15 avril 2026

  Urban areas are increasingly vulnerable to thermal extremes driven by rapid urbanization and climate change. Traditionally, thermal extremes have been monitored using Earth-observing satellites and numerical modeling frameworks. For example, land surface temperature derived from Landsat or Sentinel …

• Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature

  Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai · 7 avril 2026

  Deep learning models have shown great promise in diverse remote sensing applications. However, they often struggle to generalize across geographic regions unseen during training due to domain shifts. Domain shifts occur when data distributions differ between the training region and new target region…

• A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity

  Jiaqi Dong · 25 mars 2026

  Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme Gradient Boosting (XGBoost), Random Forest, Support Vector Regr…

• SPyCer: Semi-Supervised Physics-Guided Contextual Attention for Near-Surface Air Temperature Estimation from Satellite Imagery

  Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai · 6 mars 2026

  Modern Earth observation relies on satellites to capture detailed surface properties. Yet, many phenomena that affect humans and ecosystems unfold in the atmosphere close to the surface. Near-ground sensors provide accurate measurements of certain environmental characteristics, such as near-surface …

• Predicting Local Climate Zones using Urban Morphometrics and Satellite Imagery

  Hugo Majer, Martin Fleischmann · 3 mars 2026

  The Local Climate Zone (LCZ) framework is commonly employed to represent urban form in morphological analyses despite its mapping predominantly relies on satellite imagery. Urban morphometrics, describing urban form via numerical measures of physical aspects and spatial relationships of its elements…

• Partial recovery of meter-scale surface weather

  Jonathan Giezendanner, Qidong Yang, Eric Schmitt, Anirban Chandra, Daniel Salles Civitarese, Johannes Jakubik, Jeremy Vila, Detlef Hohl, Campbell Watson, Sherrie Wang · 27 février 2026

  Near-surface atmospheric conditions can differ sharply over tens to hundreds of meters due to land cover and topography, yet this variability is absent from current weather analyses and forecasts. It is unclear whether such meter-scale variability reflects irreducibly chaotic dynamics or contains a …

• FujiView: Multimodal Late-Fusion for Predicting Scenic Visibility

  Bryceton Bible, Shah Md Nehal Hasnaeen, Hairong Qi · 27 février 2026

  Visibility of natural landmarks such as Mount Fuji is a defining factor in both tourism planning and visitor experience, yet it remains difficult to predict due to rapidly changing atmospheric conditions. We present FujiView, a multimodal learning framework and dataset for predicting scenic visibili…