Search Results (53)

The study presented in this article explores the changing significance of the river and its impact on shaping the city’s image, using the example of the relationship between the Oder River and the city of Szczecin. The main objective was to examine how the Oder influences Szczecin’s image in the context of sustainable development. The research was based on a historical-interpretative method, employing the analysis of over three thousand postcards depicting the riverside areas of Szczecin from a period of approximately 170 years (1850–2024). The quantitative analysis of postcards was supplemented with an analysis of semantic networks. This approach made it possible to verify how representations of the river on historical postcards reflect the evolution of Szczecin’s urban identity and its connection with the idea of sustainability. The study identified the dominant meanings of the river in different historical periods, as well as characteristic views and distinctive landmarks. This allowed for an assessment of how the Oder was perceived and how these perceptions shaped the city’s image. The results indicate that Szczecin’s image has evolved over time, yet it has always remained rooted in its relationship with the river, dependent on how the Oder was perceived and valued. Today, the river represents not only an essential element of the city’s landscape and cultural identity but also a key component of its contemporary image as a sustainable city. The study contributes to understanding how riverfront imagery shapes perceptions of urban sustainability. Full article

Sign Languages are the primary communication modality of deaf communities, yet building effective Isolated Sign Language Recognition (ISLR) systems remains difficult under data limitations. In this work, we curated a sub-dataset from the DGS-Korpus focused on recognizing affirmations and negations (polar answers) in German Sign Language (DGS). We designed lightweight transformer models using landmark-based inputs and evaluated them on two tasks: the binary classification of affirmations versus negations (binary semantic recognition) and the multi-class recognition of sign variations expressing positive or negative replies (multi-class gloss recognition). The main contribution of the article, hence, relies on the exploration of models for performing polar answer recognition in DGS and the exploration of differences between performing multi-class or binary class classification. Our best binary model achieved an accuracy of 97.71% using only hand landmarks without Positional Encoding, highlighting the potential of lightweight landmark-based transformers for efficient ISLR in constrained domains. Full article

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs. Full article

Driver distraction is a key factor contributing to traffic accidents. However, in existing computer vision-based methods for driver attention state recognition, monocular camera-based approaches often suffer from low accuracy, while multi-sensor data fusion techniques are compromised by poor real-time performance. To address these limitations, this paper proposes a Real-time Driver Attention State Recognition method (RT-DASR). RT-DASR comprises two core components: Binocular Vision Depth-Compensated Head Pose Estimation (BV-DHPE) and Multi-source Temporal Bidirectional Long Short-Term Memory (MSTBi-LSTM). BV-DHPE employs binocular cameras and YOLO11n (You Only Look Once) Pose to locate facial landmarks, calculating spatial distances via binocular disparity to compensate for monocular depth deficiency for accurate pose estimation. MSTBi-LSTM utilizes a lightweight Bidirectional Long Short-Term Memory (Bi-LSTM) network to fuse head pose angles, real-time vehicle speed, and gaze region semantics, bidirectionally extracting temporal features for continuous attention state discrimination. Evaluated under challenging conditions (e.g., illumination changes, occlusion), BV-DHPE achieved 44.7% reduction in head pose Mean Absolute Error (MAE) compared to monocular vision methods. RT-DASR achieved 90.4% attention recognition accuracy with 21.5 ms average latency when deployed on NVIDIA Jetson Orin. Real-world driving scenario tests confirm that the proposed method provides a high-precision, low-latency attention state recognition solution for enhancing the safety of mining vehicle drivers. RT-DASR can be integrated into advanced driver assistance systems to enable proactive accident prevention. Full article

The increasing adoption of artificial intelligence across domains presents new opportunities to enhance access to justice. In this paper, we introduce a human-centric AI tool that utilises advances in Automatic Speech Recognition (ASR) and Large Language Models (LLMs) to facilitate semantic linking between written UK Supreme Court (SC) judgements and their corresponding hearing videos. The motivation stems from the critical role UK SC hearings play in shaping landmark legal decisions, which often span several hours and remain difficult to navigate manually. Our approach involves two key components: (1) a customised ASR system fine-tuned on 139 h of manually edited SC hearing transcripts and legal documents and (2) a semantic linking module powered by GPT-based text embeddings adapted to the legal domain. The ASR system addresses domain-specific transcription challenges by incorporating a custom language model and legal phrase extraction techniques. The semantic linking module uses fine-tuned embeddings to match judgement paragraphs with relevant spans in the hearing transcripts. Quantitative evaluation shows that our customised ASR system improves transcription accuracy by 9% compared to generic ASR baselines. Furthermore, our adapted GPT embeddings achieve an F1 score of 0.85 in classifying relevant links between judgement text and hearing transcript segments. These results demonstrate the effectiveness of our system in streamlining access to critical legal information and supporting legal professionals in interpreting complex judicial decisions. Full article

Vision-based navigation is a common solution for the critical challenge of GPS-denied Unmanned Aerial Vehicle (UAV) operation, but a research gap remains in the autonomous discovery of robust landmarks from aerial survey imagery needed for such systems. In this work, we propose a framework to fill this gap by identifying visually distinctive urban buildings from aerial survey imagery and curating them into a landmark database for GPS-free UAV localization. The proposed framework constructs semantically rich embeddings using intermediate layers from a pre-trained YOLOv11n-seg segmentation network. This novel technique requires no additional training. An unsupervised landmark selection strategy, based on the Isolation Forest algorithm, then identifies objects with statistically unique embeddings. Experimental validation on the VPAIR aerial-to-aerial benchmark shows that the proposed max-pooled embeddings, assembled from selected layers, significantly improve retrieval performance. The top-1 retrieval accuracy for landmarks more than doubled compared to typical buildings (0.53 vs. 0.31), and a Recall@5 of 0.70 is achieved for landmarks. Overall, this study demonstrates that unsupervised outlier selection in a carefully constructed embedding space yields a highly discriminative, computation-friendly set of landmarks suitable for real-time, robust UAV navigation. Full article

Mobile map landmark symbols are pivotal in conveying spatial semantics and enhancing users’ perception of digital maps. This study employs a three-factor hybrid experimental design to investigate the effects of different landmark symbol types and their color associations with annotations on search performance using eye tracking methods. Utilizing the Tobii X2-60 eye tracker, 40 participants engaged in a visual search task across three symbol types (icons, indexes, and symbols) and two color conditions (consistent and inconsistent). This study also examines the impact of gender on search performance. The results indicate that INDEX, emphasizing the landmarks’ functions and key features, most effectively improve search accuracy and efficiency while demanding the least cognitive effort. In contrast, SYMBOL type characters, with clear semantics and minimal information, require less visual attention, facilitating faster preliminary processing. Additionally, cognitive style differences between genders affect these symbols’ effectiveness in visual searches. A careful selection of symbol types and color combinations can significantly enhance user interaction with mobile maps. Full article

Landscape visual evaluation is a key method for assessing the value of visual landscape resources. This study aims to enhance the visual environment and sensory quality of urban landscapes by establishing standards for the visual comfort of urban natural landscapes. Using line-of-sight and multi-factor analysis algorithms, the method assesses spatial visibility and visual exposure of building clusters in the core urban areas of Harbin, identifying areas and viewpoints with high visual potential. Focusing on the viewpoints of landmark 3D models and the surrounding landscape’s visual environment, the study uses the city’s sky, greenery, and water features as key visual elements for evaluating the comfort of urban natural landscapes. By integrating GIS data, big data street-view photos, and image semantic recognition, spatial analysis algorithms extract both objective and subjective visual values at observation points, followed by mathematical modeling and quantitative analysis. The study explores the coupling relationship between objective physical visual values and subjective perceived visibility. The results show that 3D visual analysis effectively reveals the relationship between landmark buildings and surrounding landscapes, providing scientific support for urban planning and contributing to the development of a more distinctive and attractive urban space. Full article

Limb Length Discrepancy (LLD) is a common condition that can result in gait abnormalities, pain, and an increased risk of early degenerative osteoarthritis in the lower extremities. Epidemiological studies indicate that mild LLD, defined as a discrepancy of 10 mm or less, affects approximately 60–90% of the population. While more severe cases are less frequent, they are associated with secondary conditions such as low back pain, scoliosis, and osteoarthritis of the hip or knee. LLD not only impacts daily activities, but may also lead to long-term complications, making early detection and precise measurement essential. Current LLD measurement methods include physical examination and imaging techniques, with physical exams being simple and non-invasive but prone to operator-dependent errors. To address these limitations and reduce measurement errors, we have developed an AI-based automated lower-limb length measurement system. This method employs semantic segmentation to accurately identify the positions of the femur and tibia and extracts key anatomical landmarks, achieving a margin of error within 4 mm. By automating the measurement process, this system reduces the time and effort required for manual measurements, enabling clinicians to focus more on treatment and improving the overall quality of care. Full article

Urban commercial streets are a crucial component of urban life, serving as the central hubs of commercial activity and providing vital spaces for both residents and visitors to engage in various activities. Walkability is commonly used as a key indicator of environmental quality, playing a significant role in improving residents’ health, community interaction, and environmental quality of life. Therefore, promoting the development of a high-quality walking environment in commercial districts is crucial for fostering urban economic growth and the creation of livable cities. However, existing studies predominantly focus on the impact of the built environment on walkability at the urban scale, with limited attention given to commercial streets, particularly the influence of their physical features on walking-need perceptions. In this study, we utilized Google Street-View Panorama (GSVP) images of the Tenjin commercial district and applied the Semantic Differential (SD) method to assess four walking-need perceptions of visual walkability perception, including usefulness, comfort, safety, and attractiveness. Additionally, deep-learning-based semantic segmentation was employed to extract and calculate the physical features of the Tenjin commercial district. Correlation and regression analysis were used to investigate the impact of these physical features on the four walking-need perceptions. The results showed that the different walking-need perceptions in the Tenjin commercial district are attractiveness > safety > comfort > usefulness. Furthermore, the results show that there are significant spatial distribution differences in walking-need perceptions in the Tenjin commercial district. Safety perception is more prominent on primary roads, all four walking-need perceptions in the secondary roads at a high level, and the tertiary roads have generally lower scores for all walking-need perceptions. The regression analysis indicates that walkable space and the landmark visibility index have a significant impact on usefulness, street cleanliness emerges as the most influential factor affecting safety, greenness is identified as the primary determinant of comfort, while the landmark visibility index exerts the greatest influence on attractiveness. This study expands the existing perspectives on urban street walkability by focusing on street-level analysis and proposes strategies to enhance the visual walkability perception of commercial streets. These findings aim to better meet pedestrian needs and provide valuable insights for future urban planning efforts. Full article

The rapid growth of deep learning technology has made object detection in remote sensing images an important aspect of computer vision, finding applications in military surveillance, maritime rescue, and environmental monitoring. Nonetheless, the capture of remote sensing images at high altitudes causes significant scale variations, resulting in a heterogeneous range of object scales. These varying scales pose significant challenges for detection algorithms. To solve the scale variation problem, traditional detection algorithms compute multi-layer feature maps. However, this approach introduces significant computational redundancy. Inspired by the mechanism of cognitive scaling mechanisms handling multi-scale information, we propose a novel Scale Selection Network (SSN) to eliminate computational redundancy through scale attentional allocation. In particular, we have devised a lightweight Landmark Guided Scale Attention Network, which is capable of predicting potential scales in an image. The detector only needs to focus on the selected scale features, which greatly reduces the inference time. Additionally, a fast Reversible Scale Semantic Flow Preserving strategy is proposed to directly generate multi-scale feature maps for detection. Experiments demonstrate that our method facilitates the acceleration of image pyramid-based detectors by approximately 5.3 times on widely utilized remote sensing object detection benchmarks. Full article

In modern urban environments, visual sensors are crucial for enhancing the functionality of navigation systems, particularly for devices designed for visually impaired individuals. The high-resolution images captured by these sensors form the basis for understanding the surrounding environment and identifying key landmarks. However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features. Most existing methods rely on Convolutional Neural Networks (CNNs), whose inherent inductive biases limit their ability to capture global context and accurately detect discontinuous features such as gaps and obstructions in blind roads. To overcome these limitations, we introduce Dual-Branch Swin-CNN Net(DSC-Net), a new method that integrates the global modeling capabilities of the Swin-Transformer with the CNN-based U-Net architecture. This combination allows for the hierarchical extraction of both fine and coarse features. First, the Spatial Blending Module (SBM) mitigates blurring of target information caused by object occlusion to enhance accuracy. The hybrid attention module (HAM), embedded within the Inverted Residual Module (IRM), sharpens the detection of blind road boundaries, while the IRM improves the speed of network processing. In tests on a specialized dataset designed for blind road semantic segmentation in real-world scenarios, our method achieved an impressive mIoU of 97.72%. Additionally, it demonstrated exceptional performance on other public datasets. Full article

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m. Full article

The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression (MGWR) analysis of correlation influential factors. Using Suzhou, China, as a pilot study, this research demonstrates how these integrated methods complement each other, exploring how community conditions and resource distribution collectively bolster resilience, thereby maintaining social–spatial equity amidst pandemic disruptions. The findings reveal that uneven resource distribution exacerbates post-pandemic social stratification and spatial differentiation. The proximity of well-maintained ecological environments, such as parks or scenic landmarks, generally exhibits consistency and positive effects on “social–spatial” measurement. Simultaneously, various spatial elements influencing housing prices and social perception show geographic heterogeneity, particularly in areas farther from the central regions of Xiangcheng and Wujiang districts. This study uncovers a bilateral mechanism between social perception and spatial differentiation, aiming to delve into the interdependent relationship between social–spatial equity and built environmental factors. Furthermore, it aspires to provide meaningful references and recommendations for urban planning and regeneration policy formulation in the digital era to sustain social–spatial equity. Full article

Gastronomy represents one of the main defining national cultural elements and is essential for shaping territorial identities and for tourism development, attracting both domestic and international tourists. The landscape in the center of Bucharest has gradually changed under the influence of entrepreneurial initiatives within the hospitality industry, showing at present a rather cosmopolitan urban environment. Despite the significant number of international catering units, better adapted to global tastes, Romanian-themed restaurants represent a landmark of the capital city. In this context, our study focuses on the Romanian authentic local gastronomy offered by the themed traditional restaurants in the center of Bucharest as a stimulating factor for different types of consumers. Aiming to answer several research questions, this research has a complex multi-fold methodological approach, appealing to triangulation which gathered, as main analytic methods, mapping, semantic analyses, and text visualisation, and the interview method (originally and appropriately applied for this case study to experienced employees). The main results show a complex gastronomic landscape that gathers various types of restaurants but outlines those with a Romanian ethnic theme in the center of Bucharest. The study of Romanian restaurants’ menus reveals elements of authenticity (e.g., traditional dishes and their regional denominations, local rural ingredients, old recipes, and cuisine techniques) as factors of attractiveness for consumers and as competitive advantages in their market. Moreover, interviews with staff representatives outline restaurants’ atmosphere, originality, and price–quality ratio of their food as the main attractive elements for both autochtonous customers and tourists and which offer an advantage in the market. The present study may interest multiple stakeholders, focusing on the development and evolution of the hospitality industry in Romania. Full article