Street Canyon Research Articles (Page 1)

Abstract. Positioning, Navigation, and Timing (PNT) solutions are fundamental for autonomous driving, ensuring reliable localization for safe vehicle control in diverse environments. While GNSS-based systems provide absolute positioning, they become unreliable in GNSS-denied scenarios such as urban canyons or tunnels. Dead reckoning techniques, including Visual Odometry (VO), offer an alternative by estimating motion from onboard sensors. Integrating these methods with deep learning (DL) has shown potential for enhancing robustness, particularly in challenging conditions. This study, part of the VAIPOSA ESA project, investigates the performance of VO solutions under various environmental conditions using a simulation-based approach. The CARLA simulator provides controlled testing scenarios, enabling the evaluation of VO accuracy across different weather conditions, illumination changes, and dynamic environments. A synthetic stereo setup enables capturing error-free ground truth trajectories and fair evaluation of the VO methods. Multiple sequences are analyzed, reflecting real-world challenges such as poor visibility, texture variations, and occlusions. The findings highlight the influence of environmental factors and dynamic objects on VO performance and the role of DL in mitigating common failure modes.

This study investigates how future climate change will influence urban microclimates in Athens, Greece, focusing on two representative districts classified as Local Climate Zones (LCZ2 and LCZ3). Using the ENVI-met model, microclimate simulations were conducted to assess projected air temperature variations under moderate (Representative Concentration Pathways RCP4.5) and high (RCP8.5) emission scenarios for mid- and late-century conditions. The analysis reveals a consistent warming trend across both districts, with average air temperature increases of approximately 2–3 °C by mid-century and up to 4.5 °C by the end of the century. Morphological characteristics were found to significantly affect thermal behavior: areas with wider street canyons exhibited higher temperatures due to increased solar exposure, whereas shaded inner courtyards remained relatively cooler. The study’s novelty lies in its integration of high-resolution urban microclimate modeling with climate scenario analysis for a Mediterranean metropolis, a combination seldom explored in previous research. The findings underline the importance of incorporating urban morphology into climate adaptation planning, supporting the design of low-carbon and thermally resilient urban forms in densely built environments.

This paper presents a real-time handover and link assignment framework for low-Earth-orbit (LEO) satellite networks operating in dense urban canyons. The proposed Markov chain-guided simulated annealing (MCSA) algorithm optimizes user-to-satellite assignments under dynamic channel and capacity constraints. By incorporating Markov chains to guide state transitions, MCSA achieves faster convergence and more effective exploration than conventional simulated annealing. Simulations conducted in Ku-band urban canyon environments show that the framework achieves an average user satisfaction of about 97%, providing an approximately 10% improvement over genetic algorithm (GA) results. It also delivers 10–15% higher resource utilization, lower blocking rates comparable to integer linear programming (ILP), and superior runtime scalability with linear complexity O(k·|U|·|S|). These results confirm that MCSA provides a scalable and robust real-time mobility management solution for next-generation LEO satellite systems.

Improved tightly-coupled PPP/MEMS-INS integration with adaptive code pseudorange weighting and LSTM-based smoothing for urban canyon navigation

Evaluating permeable pavements' impact on the thermal environment in street canyons through outdoor experiments under hot and humid background climate

Two-step High-resolution Reconstruction of Mean Flow Field and Reynolds Stress Distributions using Physics-Informed Neural Networks in a Two-Dimensional Street Canyon

ABSTRACT In this study, a unified analytical solution for the seismic response of a symmetrical triangular hill or canyon under SH‐wave incidence is developed using novel coordinate transformations in the complex domain. First, the entire solution domain is divided into two subregions. Wave functions with undetermined coefficients are then constructed in each subregion such that the zero‐stress boundary conditions on the free surface are inherently satisfied. Second, the continuity boundary conditions along the interface are enforced to determine the unknown coefficients in the complex domain. Finally, a parametric study is conducted to investigate the differences in the effects of hills and canyons on SH‐wave scattering. The results indicate that the seismic response characteristics of a symmetrical triangular hill or canyon under SH wave incidence exhibit different patterns. The seismic response of hills shows reduced displacement amplitudes with increasing incidence angle, while that of canyons exhibits opposite trends. Under oblique SH‐wave incidence, peak displacement amplitudes are typically found on the right slope of hills and the left slope of canyons. The height‐to‐span ratio of hills significantly affects peak displacement amplitudes, but the depth‐to‐span ratio of canyons has a negligible effect.

Accurate prediction of urban wind flow is essential for urban planning and environmental assessment. Classical computational fluid dynamics (CFD) methods are computationally expensive, while machine learning approaches often lack explainability and generalizability. To address the limitations of both approaches, we propose Diff-KNN, a hybrid method that combines Coarse-Scale CFD simulations with a K-Nearest Neighbors (KNN) model trained on the residuals between coarse- and fine-scale CFD results. Diff-KNN reduces velocity prediction errors by up to 83.5% compared to Pure-KNN and 56.6% compared to coarse CFD alone. Tested on the AIJE urban dataset, Diff-KNN effectively corrects flow inaccuracies near buildings and within narrow street canyons, where traditional methods struggle. This study demonstrates how residual learning can bridge physics-based and data-driven modeling for accurate and interpretable fine-scale urban wind prediction.

Machine Learning-Driven Source Apportionment of HONO in High-Ammonia Street Canyon Microenvironments

The intensification of thermal stress in cities due to urbanization and climate change underscores the urgent need to improve outdoor habitability. This study analyses the influence of three opaque façade technologies—traditional, lightweight and external thermal insulation composite systems—combined with two albedo levels (0.30 and 0.80), on summer outdoor conditions in Mendoza (Argentina), Madrid (Spain) and Campinas (Brazil). Using a calibrated microclimatic model with ENVI-met v5.6 software, a digital replica of a 10-storey urban canyon was simulated to generate 18 scenarios, assessing the effect of façade thermal mass and reflectivity on the urban microclimate. The results show that (i) scenarios that mainly affect air temperature (AT) are those that modify the thermal mass of the façade technologies. For example, traditional technology with a low albedo reduce maximum AT by up to 1.2 °C in Campinas, 0.89 °C in Mendoza, and 0.81 °C in Madrid compared to light technology with the same albedo level. (ii) Mean radiant temperature (MRT) increases significantly in scenarios involving lightweight façade by 4.53 °C in Madrid, 4.46 °C in Mendoza, and 3.39 °C in Campinas. Conversely, increasing façade albedo further amplifies MRT due to multiple reflections in urban canyons with increases of 6.50 °C in Campinas, 6.09 °C in Mendoza, and 5.33 °C in Madrid. The impact is more pronounced with traditional façades. (iii) Traditional façades and low-albedo ETIC systems experience the fewest hours of very high thermal stress (UTCI > 38 °C), whereas lightweight façades increase exposure to extreme heat. Overall, air temperature is primarily determined by façade thermal mass, mean radiant temperature by surface reflectivity, and thermal comfort by the combined effect of both. These findings confirm that high reflectivity can be counterproductive in dense urban canyons, emphasizing the importance of climate- and morphology-sensitive façade strategies for urban resilience.

Traffic-related PM2.5 worsens street air quality and affects pedestrian health, and the benefits of street trees in reducing air pollution within street canyons are still controversial. To clarify the influence of street tree parameters on PM2.5 dispersion, tree height (TH), tree spacing (TS) and leaf area density (LAD) were selected from three different categories to represent the common characteristics of street tree species. Field measurements were conducted to validate ENVI-met model. We carried out 64 simulation scenarios with different parameters (TH, TS and LAD) and a treeless scenario in idealized street canyons (aspect ratio = 1:1) under oblique wind direction. The results showed that the 3D surface plots can effectively display the spatial distribution of PM2.5 within street canyons. PM2.5 concentration increased after planting trees; the leeward side showed higher concentrations than windward side, and the downstream showed higher concentration than upstream area. TH and LAD significantly influenced PM2.5 concentration, while TS had less influence, and their effects differed in spatial position. PM2.5 reduction efficiency (RE) varied with different street tree parameters on the windward side and leeward sides. Sky view factor (SVF) was significantly negatively correlated with PM2.5 concentration, while green coverage ratio (GCR) and green plot ratio (GPR) were inversely correlated. TH = 6m, TS = 6m and LAD = 1.0 m2 m-3 could be a suitable parameter combination for street tree planting or pruning. These results could provide practical suggestions for urban planning and landscape design to improve street air quality and promote sustainable urban development.

Numerical weather prediction (NWP) models, coupled with urban parameterizations, play a crucial role in understanding and forecasting meteorological conditions within urban environments. In the mesoscale NWP model COSMO, only one urban parameterization, TERRA_URB, is available in the model’s operational version. TERRA_URB describes the city as a flat surface with modified physical properties in accordance with the urban canyon geometry. In this study, we have coupled the latest version 6.0 of the COSMO atmospheric model with a more sophisticated urban canopy model, TEB (Town Energy Balance), which explicitly simulates the energy exchange between the facets of the urban canyon. Here, we present the coupling approach and assessment of the model’s sensitivity to urban schemes of different complexity (TEB and TERRA_URB) over the Moscow region for August 2022. Despite using the same external parameters for both schemes, simulations demonstrate notable differences in modeled temperature, with TEB generally producing lower nighttime and morning temperatures. This leads to a greater underestimation of the urban heat island intensity in TEB when compared with the observations but improves the modeled diurnal cycle of the urban temperature. We attribute the observed temperature discrepancies to the different descriptions of heat conductivity and storage within urban surfaces. Although there are no clear advantages to using a more complex parameterization in terms of model air temperature errors, TEB offers more options to fine-tune input parameters and takes into account additional processes, in particular those associated with building heating and cooling, as well as with urban green infrastructure.

We propose a comprehensive literature review based on big data and V2X research to find promising tools to detect vehicles for traffic research and provide safe autonomous vehicle (AV) traffic. Presented data sources can provide real-time data for V2X systems and offline databases from VATnets for micro- and macro-modeling in traffic research. The authors want to present a set of sources that are not based on GNSS and other systems that could be interrupted by high-rise buildings and dense smart city infrastructure, as well as review of big data sources in traffic modeling that can be useful in future traffic research. Both reviews findings are summarized in tables at the end of the review sections of the paper. The authors added propositions in the form of two hypotheses on how traffic models can obtain data in the urban canyon connected environment scenario. The first hypothesis uses Roadside Units (RSUs) to retrieve data in similar ways to cellular data in traffic research and proves that this source is data rich. The second one acknowledges Bluetooth/Wi-Fi scanners’ research potential in V2X environments.

Modelling nitrogen dioxide dispersion in urban street canyons through sensor-based emission assessment.

Influence of geometric parameters of façade protruding ribs on turbulent flow statistics in street canyons: A large-eddy simulation study

Abstract This study investigates the role of urban morphology in shaping wind suitability and ventilation potential across Manila, Philippines, by evaluating six spatial parameters: street density, building density, mean wind speed, intersection density, height-to-area ratio, and street canyon profile. Employing a multi-criteria decision analysis (MCDA) framework, the study integrates these variables into a composite suitability score, facilitating a data-driven classification of urban zones based on their capacity to support wind energy interventions. The findings reveal a spatial gradient in suitability, indicating that high and moderate wind-suitable areas are concentrated in waterfront districts and zones characterized by open street networks and lower building compactness. Conversely, the urban core demonstrates predominantly low suitability due to a dense built form and constrained airflow. While tall buildings can channel wind through canyon effects, their arrangement often disrupts broader circulation, resulting in localized stagnation or turbulence. The synthesized wind suitability map emphasizes the necessity of aligning building height, spacing, and orientation with natural wind corridors to enhance both ventilation and the feasibility of micro-scale urban wind technologies. Furthermore, the research highlights the imperative for morphology-aware urban planning in tropical megacities, where high land-use compactness presents both challenges and opportunities for sustainable energy integration. Future research should incorporate long-term wind monitoring and computational fluid dynamics (CFD) simulations to validate and refine these spatial insights.

Analysis of forced convective heat transfer at the facades of street canyon: Effect of wind direction and tree planting

Mixed Convection in Urban Street Canyons under Non-Uniform Wall Heating: Water-Channel Experiments and RANS CFD Simulations

To address the high-precision navigation requirements of urban low-altitude electric vertical take-off and landing (eVTOL) aircraft in environments where global navigation satellite systems (GNSSs) are denied and under complex urban terrain conditions, a terrain-matching optimization algorithm based on light detection and ranging (LiDAR) is proposed. Given the issues of GNSS signal susceptibility to occlusion and interference in urban low-altitude environments, as well as the error accumulation in inertial navigation systems (INSs), this algorithm leverages LiDAR point cloud data to assist in constructing a digital elevation model (DEM). A terrain-matching optimization algorithm is then designed, incorporating enhanced feature description for key regions and an adaptive random sample consensus (RANSAC)-based misalignment detection mechanism. This approach enables efficient and robust terrain feature matching and dynamic correction of INS positioning errors. The simulation results demonstrate that the proposed algorithm achieves a positioning accuracy better than 2 m in complex scenarios such as typical urban canyons, representing a significant improvement of 25.0% and 31.4% compared to the traditional SIFT-RANSAC and SURF-RANSAC methods, respectively. It also elevates the feature matching accuracy rate to 90.4%; meanwhile, at a 95% confidence level, the proposed method significantly increases the localization success rate to 96.8%, substantially enhancing the navigation and localization accuracy and robustness of eVTOLs in complex low-altitude environments.

The subject of this article is visual place recognition (VPR), specifically matching satellite images with images captured by unmanned aerial vehicles (UAVs). VPR is critical for autonomous UAV navigation, particularly in GPS-denied environments such as urban canyons or areas with significant infrastructure coverage where GNSS signals are unreliable. Despite its practical importance, accurately matching UAV images to satellite imagery remains challenging due to significant viewpoint, scale, illumination, and texture discrepancies. Traditional approaches that rely on handcrafted descriptors or classical local features often fail under such cross-view conditions. This study aims to design a robust visual place recognition method for matching UAV and satellite imagery, employing deep learning-based embeddings and advanced color normalization to improve reliability across cross-view scenarios. The tasks addressed in this article are: firstly, designing a YOLO-based method is designed for extracting global image embeddings, which utilizes YOLO’s multi-scale feature extraction capabilities to encode semantically significant landmarks in the scene. Second, a novel preprocessing technique based on aligning statistical color distributions between UAV and satellite images was developed and implemented to enhance their visual congruence. Finally, these components are integrated into a complete VPR system and evaluated for effectiveness using the challenging VPAIR dataset, emphasizing urban settings. The methods employed include deep learning techniques, particularly fine-tuning a YOLO11 neural network on a dataset specifically annotated for building segmentation. Statistical alignment techniques based on cumulative distribution functions (CDF) were used to standardize image appearances between the two distinct image domains. Conclusions. The experiments demonstrate significant improvements in UAV-to-satellite image matching performance using the proposed method. Fine-tuning YOLO11 specifically for building segmentation resulted in a robust embedding generation method that achieved high segmentation accuracy (F1-score of 0.722). The color preprocessing technique further improved the recognition performance, with Recall@1 reaching 19.5% for urban terrain within a localization radius of 3, substantially outperforming the traditional methods. This study provides an effective solution for UAV localization tasks, particularly in complex urban environments, highlighting the importance of integrated embedding extraction and domain-specific image preprocessing in cross-view visual place recognition.