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Operational machine learning for park-scale irrigation to support urban cooling
Mesut Ko\c{c}yi\u{g}it, Bahman Javadi, Russell Thomson, Sebastian Pfautsch, Oliver Obst
https://arxiv.org/abs/2508.11700 …

Operational machine learning for park-scale irrigation to support urban cooling
Urban parks can mitigate local heat, yet irrigation control is usually tuned for water savings rather than cooling. We report on SIMPaCT (Smart Irrigation Management for Parks and Cool Towns), a park-scale deployment that links per-zone soil-moisture forecasts to overnight irrigation set-points in support of urban cooling. SIMPaCT ingests data from 202 soil-moisture sensors, 50 temperature-relative humidity (TRH) nodes, and 13 weather stations, and trains a per-sensor k-nearest neighbours (kNN)…

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
Sanghyun Kim, Jiwon Seo
https://arxiv.org/abs/2507.12706

Enhancing Urban GNSS Positioning Reliability via Conservative Satellite Selection Using Unanimous Voting Across Multiple Machine Learning Classifiers
In urban environments, global navigation satellite system (GNSS) positioning is often compromised by signal blockages and multipath effects caused by buildings, leading to significant positioning errors. To address this issue, this study proposes a robust enhancement of zonotope shadow matching (ZSM)-based positioning by employing a conservative satellite selection strategy using unanimous voting across multiple machine learning classifiers. Three distinct models - random forest (RF), gradient …

Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
Islam M. Tanash, Ayush Kumar Dwivedi, Taneli Riihonen
https://arxiv.org/abs/2506.15338

Urban RIS-Assisted HAP Networks: Performance Analysis Using Stochastic Geometry
This paper studies a high-altitude platform (HAP) network supported by reconfigurable intelligent surfaces (RISs). The practical irregular placement of HAPs and RISs is modeled using homogeneous Poisson point processes, while buildings that cause blockages in urban areas are modeled as a Boolean scheme of rectangles. We introduce a novel approach to characterize the statistical channel based on generalized Beta prime distribution. Analytical expressions for coverage probability and ergodic capa…

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Nichula Wasalathilaka, Mathivathanan Piratheepan, Roshan Godaliyadda, Mervyn Ekanayake, Vijitha Herath
https://arxiv.org/abs/2507.11523

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applica…

The Hierarchical Morphotope Classification: A Theory-Driven Framework for Large-Scale Analysis of Built Form
Martin Fleischmann, Krasen Samardzhiev, Anna Br\'azdov\'a, Daniela Dan\v{c}ejov\'a, Lisa Winkler
https://arxiv.org/abs/2509.10083

The Hierarchical Morphotope Classification: A Theory-Driven Framework for Large-Scale Analysis of Built Form
Built environment, formed of a plethora of patterns of building, streets, and plots, has a profound impact on how cities are perceived and function. While various methods exist to classify urban patterns, they often lack a strong theoretical foundation, are not scalable beyond a local level, or sacrifice detail for broader application. This paper introduces the Hierarchical Morphotope Classification (HiMoC), a novel, theory-driven, and computationally scalable method of classification of built …

Modeling Multistability and Hysteresis in Urban Traffic Dynamics
Jung-Hoon Jung, Young-Ho Eom
https://arxiv.org/abs/2507.06659 https://

Modeling Multistability and Hysteresis in Urban Traffic Dynamics
Growing evidence suggests that the macroscopic functional states of urban road networks exhibit multistability and hysteresis, but microscopic mechanisms underlying these phenomena remain elusive. Here, we demonstrate that in real-world road networks, the recovery process of congested roads is not spontaneous, as assumed in existing models, but is hindered by connected congested roads, and such hindered recovery can lead to the emergence of multistability and hysteresis in urban traffic dynamic…

On The Road - To Xi’An ⛅️
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📷 Minolta Hi-Matic AF
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Nested economies of scale in city mass
Kangning Huang, Mingzhen Lu
https://arxiv.org/abs/2507.03960 https://arxiv.org/pdf/2507.03960

Nested economies of scale in city mass
A longstanding puzzle in urban science is whether there's an intrinsic match between human populations and the mass of their built environments. Previous findings have revealed various urban properties scaling nonlinearly with population, yet existing models of city built mass are still dominated by per-capita linear thinking. Our analysis of >3,000 cities globally reveals universal sublinear scaling of city mass with population at both the city (\{beta}=0.90) and neighborhood levels (δ=0.75).…

Channel Charting in Smart Radio Environments
Mahdi Maleki, Reza Agahzadeh Ayoubi, Marouan Mizmizi, Umberto Spagnolini
https://arxiv.org/abs/2508.07305 https://

Channel Charting in Smart Radio Environments
This paper introduces the use of static electromagnetic skins (EMSs) to enable robust device localization via channel charting (CC) in realistic urban environments. We develop a rigorous optimization framework that leverages EMS to enhance channel dissimilarity and spatial fingerprinting, formulating EMS phase profile design as a codebook-based problem targeting the upper quantiles of key embedding metric, localization error, trustworthiness, and continuity. Through 3D ray-traced simulations of…