Search Results (508)

In the context of the energy crisis and the urgency of passive design in contemporary architecture, this study focuses on the Tujia-style Sanheyuan in southeastern Chongqing, China, which is highly adaptable to local climatic conditions. Using field surveys, architectural mapping, computational fluid dynamics numerical simulations, and multi-parameter comparative analysis, this study systematically explores the relationship between the geometric form of the Sanheyuan and its courtyard ventilation performance. Based on the Tujia construction scale modulus, this study summarizes the basic prototype of the Sanheyuan, analyzes the selection paths of its three sets of construction parameters, and constructs 48 typical courtyard models for wind environment simulation. By introducing five evaluation indicators—wind speed uniformity coefficient, proportion of strong wind zone area, proportion of calm wind zone area, and unit area wind rate—this study comprehensively assesses the impact of Sanheyuan design parameters on courtyard wind environment adaptability. This study concludes that specific spatial design parameters of the Tujia-style Sanheyuan significantly influence wind environment adaptability, offering quantitative guidance for climate-responsive and culturally informed architectural design. This study found that the optimal side room width-to-depth ratio is [1.00, 0.86, 0.83]; the optimal ridge height-to-stilt height ratio is [4.29, 8.00, 2.96]; and the optimal building footprint-to-side room area ratio is [3.01, 5.06, 4.75]. Full article

Scene text understanding, serving as a cornerstone technology for autonomous navigation, document digitization, and accessibility tools, has witnessed a paradigm shift from traditional methods relying on handcrafted features and multi-stage processing pipelines to contemporary deep learning frameworks capable of learning hierarchical representations directly from raw image inputs. This survey distinctly categorizes modern scene text recognition (STR) methodologies into three principal paradigms: two-stage detection frameworks that employ region proposal networks for precise text localization, single-stage detectors designed to optimize computational efficiency, and specialized architectures tailored to handle arbitrarily shaped text through geometric-aware modeling techniques. Concurrently, an in-depth analysis of text recognition paradigms elucidates the evolutionary trajectory from connectionist temporal classification (CTC) and sequence-to-sequence models to transformer-based architectures, which excel in contextual modeling and demonstrate superior performance. In contrast to prior surveys, this work uniquely emphasizes several key differences and contributions. Firstly, it provides a comprehensive and systematic taxonomy of STR methods, explicitly highlighting the trade-offs between detection accuracy, computational efficiency, and geometric adaptability across different paradigms. Secondly, it delves into the nuances of text recognition, illustrating how transformer-based models have revolutionized the field by capturing long-range dependencies and contextual information, thereby addressing challenges in recognizing complex text layouts and multilingual scripts. Furthermore, the survey pioneers the exploration of critical research frontiers, such as multilingual text adaptation, enhancing model robustness against environmental variations (e.g., lighting conditions, occlusions), and devising data-efficient learning strategies to mitigate the dependency on large-scale annotated datasets. By synthesizing insights from technical advancements across 28 benchmark datasets and standardized evaluation protocols, this study offers researchers a holistic perspective on the current state-of-the-art, persistent challenges, and promising avenues for future research, with the ultimate goal of achieving human-level scene text comprehension. Full article

This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R² = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article

The reconstruction of historic buildings requires the integration of diverse data sources, both geometric and non-geometric. This study presents a multi-source data analysis methodology for heritage reconstruction using 3D modeling and Historic Building Information Modeling (HBIM). The proposed approach combines geometric data, including point clouds acquired via Terrestrial Laser Scanning (TLS), with architectural documentation and non-geometric information such as photographs, historical records, and technical descriptions. The case study focuses on a wooden Orthodox church in Żmijowiska, Poland, analyzing geometric changes in the structure over multiple decades. The reconstruction process integrates modern surveys with archival sources and, in the absence of complete geometric data, utilizes semantic, topological, and structural information. Geometric datasets from the 1990s, 1930s, and the turn of the 20th century were analyzed, supplemented by intermediate archival photographs and technical documentation. This integrated method enabled the identification of transformation phases and verification of discrepancies between historical records and the building’s actual condition. The findings confirm that the use of HBIM and multi-source data fusion facilitates accurate reconstruction of historical geometry and supports visualization of spatial changes across decades. Full article

The accelerated evolution of remote sensing technology has intensified the demand for real-time tile map generation, highlighting the limitations of conventional mapping approaches that rely on manual cartography and field surveys. To address the critical need for rapid cartographic updates, this study presents a novel multi-stage generative framework that synergistically integrates Generative Adversarial Networks (GANs) with Diffusion Denoising Models (DMs) for high-fidelity map generation from remote sensing imagery. Specifically, our proposed architecture first employs GANs for rapid preliminary map generation, followed by a cascaded diffusion process that progressively refines topological details and spatial accuracy through iterative denoising. Furthermore, we propose a hybrid attention mechanism that strategically combines channel-wise feature recalibration with coordinate-aware spatial modulation, enabling the enhanced discrimination of geographic features under challenging conditions involving edge ambiguity and environmental noise. Quantitative evaluations demonstrate that our method significantly surpasses established baselines in both structural consistency and geometric fidelity. This framework establishes an operational paradigm for automated, rapid-response cartography, demonstrating a particular utility in time-sensitive applications including disaster impact assessment, unmapped terrain documentation, and dynamic environmental surveillance. Full article

An accurate assessment of sidewalk conditions is critical for ensuring compliance with the Americans with Disabilities Act (ADA), particularly to safeguard mobility for wheelchair users. This paper presents a novel 3D reconstruction framework based on neural radiance field (NeRF), which utilize a monocular video input from consumer-grade cameras to generate high-fidelity 3D models of sidewalk environments. The framework enables automatic extraction of ADA-relevant geometric features, including the running slope, the cross slope, and vertical displacements, facilitating an efficient and scalable compliance assessment process. A comparative study is conducted across three surveying methods—manual measurements, LiDAR scanning, and the proposed NeRF-based approach—evaluated on four sidewalks and one curb ramp. Each method was assessed based on accuracy, cost, time, level of automation, and scalability. The NeRF-based approach achieved high agreement with LiDAR-derived ground truth, delivering an F1 score of 96.52%, a precision of 96.74%, and a recall of 96.34% for ADA compliance classification. These results underscore the potential of NeRF to serve as a cost-effective, automated alternative to traditional and LiDAR-based methods, with sufficient precision for widespread deployment in municipal sidewalk audits. Full article

Wormholes are non-trivial topological structures that arise as exact solutions to Einstein’s field equations, theoretically connecting distinct regions of spacetime via a throat-like geometry. While static traversable wormholes necessarily require exotic matter that violates the classical energy conditions, subsequent studies have sought to minimize such violations by introducing time-dependent geometries embedded within cosmological backgrounds. This review provides a comprehensive survey of evolving wormhole solutions, emphasizing their formulation within both general relativity and alternative theories of gravity. We explore key developments in the construction of non-static wormhole spacetimes, including those conformally related to static solutions, as well as dynamically evolving geometries influenced by scalar fields. Particular attention is given to the wormholes embedded into Friedmann–Lemaître–Robertson–Walker (FLRW) universes and de Sitter backgrounds, where the interplay between the cosmic expansion and wormhole dynamics is analyzed. We also examine the role of modified gravity theories, especially in hybrid metric–Palatini gravity, which enable the realization of traversable wormholes supported by effective stress–energy tensors that do not violate the null or weak energy conditions. By systematically analyzing a wide range of time-dependent wormhole solutions, this review identifies the specific geometric and physical conditions under which wormholes can evolve consistently with null and weak energy conditions. These findings clarify how such configurations can be naturally integrated into cosmological models governed by general relativity or modified gravity, thereby contributing to a deeper theoretical understanding of localized spacetime structures in an expanding universe. Full article

Quantifying the slip rate along geometrically complex strike-slip faults is essential for understanding kinematics and strain partitioning in orogenic systems. The Karlik Tagh forms the easternmost terminus of Tian Shan and represents a critical restraining bend along the sinistral strike-slip Gobi-Tian Shan Fault System. The North Karlik Tagh Fault (NKTF) is an important fault demarcating the north boundary of the Karlik Tagh. While structurally significant, it is poorly understood in terms of its late Quaternary tectonic activity. In this study, we analyze the offset geomorphology based on interpretations of satellite imagery, field survey, and digital elevation models derived from structure-from-motion (SfM), and we provide the first quantitative constraints on the late-Quaternary slip rate using the abandonment age of deformed fan surfaces and river terraces constrained by the ¹⁰Be cosmogenic dating method. Our results reveal that the NKTF can be divided into the Yanchi and Xiamaya segments based on along-strike variations. The NW-striking Yanchi segment exhibits thrust faulting with a 0.07–0.09 mm/yr vertical slip, while the NE-NEE-striking Xiamaya segment displays left-lateral slip at 1.1–1.4 mm/yr since 180 ka. In easternmost Tian Shan, the interaction between thrust and sinistral strike-slip faults forms a transpressional regime. These left-lateral faults, together with those in the Gobi Altai, collectively facilitate eastward crustal escape in response to ongoing Indian indentation. Full article

This study was conducted in the northeast of the Tadla plain, within the Beni Mellal-Khenifra region of Morocco. The primary objective is to elucidate the geometric and hydrogeological characteristics of this aquifer by analyzing and interpreting data from deep boreholes as well as gravimetric and electrical measurements using GIS analysis. First, the regional gradient was established. Then, the initial data were extracted. Subsequently, based on the extracted data, a gravity map was created. The investigation of the Bouguer anomaly’s gravity map exposes the presence of a regional gradient, with values varying from −100 mGal in the South to −30 mGal in the North of the area. These Bouguer anomalies often correlate with exposed basement rock areas and variations in the thickness of sedimentary layers across the study area. The analysis of existing electrical survey and deep drilling data confirms the results of the gravimetry survey after applying different techniques such as horizontal gradient and upward extension on the gravimetric map. The findings enabled us to create a structural map highlighting the fault systems responsible for shaping the study area’s structure. The elaborated structural map serves as an indispensable geotectonic reference, facilitating the delineation of subsurface heterogeneities and providing a robust foundation for further hydrogeological assessments in the Tadla Plain. Full article

Lithological identification of outcrops in complex geological settings plays a crucial role in hydrocarbon exploration and geological modeling. To address the limitations of traditional field surveys, such as low efficiency and high risk, we proposed an intelligent lithology recognition method, SG-RFGeo, for terrestrial laser scanning (TLS) outcrop point clouds, which integrates spectral and geometric features. The workflow involves several key steps. First, lithological recognition units are created through regular grid segmentation. From these units, spectral reflectance statistics (e.g., mean, standard deviation, kurtosis, and other related metrics), and geometric morphological features (e.g., surface variation rate, curvature, planarity, among others) are extracted. Next, a double-layer random forest model is employed for lithology identification. In the shallow layer, the Gini index is used to select relevant features for a coarse classification of vegetation, conglomerate, and mud–sandstone. The deep-layer module applies an optimized feature set to further classify thinly interbedded sandstone and mudstone. Geological prior knowledge, such as stratigraphic attitudes, is incorporated to spatially constrain and post-process the classification results, enhancing their geological plausibility. The method was tested on a TLS dataset from the Yueyawan outcrop of the Qingshuihe Formation, located on the southern margin of the Junggar Basin in China. Results demonstrate that the integration of spectral and geometric features significantly improves classification performance, with the Macro F1-score increasing from 0.65 (with single-feature input) to 0.82. Further, post-processing with stratigraphic constraints boosts the overall classification accuracy to 93%, outperforming SVM (59.2%), XGBoost (67.8%), and PointNet (75.3%). These findings demonstrate that integrating multi-source features and geological prior constraints effectively addresses the challenges of lithological identification in complex outcrops, providing a novel approach for high-precision geological modeling and exploration. Full article

In recent years, automatic segmentation and classification of data from digital surveys have taken a central role in built heritage studies. However, the application of Machine and Deep Learning (ML and DL) techniques for semantic segmentation of point clouds is complex in the context of historic architecture because it is characterized by high geometric and semantic variability. Data quality, subjectivity in manual labeling, and difficulty in defining consistent categories may compromise the effectiveness and reproducibility of the results. This study analyzes the influence of three key factors—annotator specialization, point cloud density, and sensor type—in the supervised classification of architectural elements by applying the Random Forest (RF) algorithm to datasets related to the architectural typology of the Franciscan cloister. The main innovation of the study lies in the development of an advanced feature selection technique, based on multibeam statistical analysis and evaluation of the p-value of each feature with respect to the target classes. The procedure makes it possible to identify the optimal radius for each feature, maximizing separability between classes and reducing semantic ambiguities. The approach, entirely in Python, automates the process of feature extraction, selection, and application, improving semantic consistency and classification accuracy. Full article

Field surveys focused on detailed mapping and measurements of coseismic surface ruptures along the causative fault of the 6 February 2023, Mw 7.8 Kahramanmaraş earthquake. The aim was filling gaps in the previously available surface-faulting trace, validating the accuracy of data obtained from remote sensing, refining fault offset estimates, and gaining a deeper understanding of both the local and overall patterns of the main rupture strands. Measurements and observations confirm dominating sinistral strike-slip movement. An integrated and comprehensive slip distribution curve shows peaks reaching over 700 cm, highlighting the near-fault expressing up to 70% of the deep net offset. In general, the slip distribution curve shows a strong correlation with the larger north-eastern deformation of the geodetic far field dislocation field and major deep slip patches. The overall rupture trace is generally straight and narrow with significant geometric complexities at a local scale. This results in transtensional and transpressional secondary structures, as multi-strand positive and negative tectonic flowers, hosting different patterns of the mole-tracks at the outcrop scale. The comprehensive and detailed field survey allowed characterizing the structural framework and geometric complexity of the surface faulting, ensuring accurate offset measurements and the reliable interpretation of both morphological and geometric features. Full article

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks. Full article

Blue-and-white porcelain patterns embody rich symbolic meanings and play a pivotal role in the transmission of Chinese intangible cultural heritage. However, their contemporary application often faces challenges due to complex visual forms and contextual interpretations. This study adopts a semiotic perspective to reinterpret blue-and-white porcelain motifs as cultural heritage symbols, aiming to assess their potential for sustainable preservation and modern revitalization. A hybrid evaluation framework is proposed, combining Grey System Theory and the Fuzzy Evaluation Method to quantitatively analyze 40 representative patterns across five key dimensions: cultural symbolism, esthetic value, communicative potential, modern applicability, and sustainability. Data were collected from expert panels, public surveys, and market performance, with the Analytic Hierarchy Process (AHP) employed to determine the relative importance of each dimension. The results reveal that plant and geometric patterns exhibit high adaptability and symbolic clarity, making them ideal for reinterpretation in modern design. Conversely, complex narrative and animal-based motifs demonstrate weaker performance in communicative efficiency and sustainability, indicating the need for visual simplification and semantic transformation. This study provides a theoretical and methodological foundation for the revitalization of traditional porcelain heritage in contemporary design practice, contributing to the global dissemination and sustainable development of cultural heritage symbols. Full article

YouthMappers chapters, utilizing OpenStreetMap (OSM), play a pivotal role in tackling climate challenges through education and activism. This study investigates the influence of a booster grant project on enhancing Climate Activism and Education efforts through YouthMappers chapters in Sri Lanka. Through a geometric approach, the research integrates measurable survey data from OSM platform data from 223 YouthMappers chapter respondents at four (04) universities in Sri Lanka to evaluate five critical factors/dimensions: Capacity Building and Funding Support (CBFS), Climate Activism and Education (CAE), Community Engagement and Collaboration (CEC), Technical Skills and Resources (TSR), and Sustainability and Policy Integration (SPI). The Friedman test confirmed statistically significant differences across all factors’ variables (p < 0.001), highlighting strengths in technical competence and educational integration, with gaps identified in community engagement and sustainability. A Radial Basis Function (RBF) model revealed moderate predictive accuracy, excelling in variables like CAE and TSR but indicating higher error rates in SPI and CEC. Practical outcomes include flood risk maps, curriculum-integrated teaching schemes, and localized mapping workshops. These results underscore the booster grant’s role in enabling impactful, youth-led geospatial initiatives. However, challenges such as internet access, training gaps, and language barriers remain. This study recommends expanding student and community participation, refining training strategies, and integrating OSM into university curricula. These scalable interventions offer valuable insights for replication in other vulnerable regions, enhancing climate resilience through community-driven, data-informed youth engagement. Full article