Search Results (45)

Indoor positioning remains a significant challenge, particularly in environments such as museums, where the installation of specialized positioning infrastructure may be impractical. Recent advances in image processing offer effective and precise methods for object recognition, presenting a viable alternative. This study explores the feasibility of employing real-time image processing techniques for identifying points of interest (POIs) within museum settings. It outlines the ideation, design, development, and evaluation of an image-based POI identification system implemented in a real-world environment. To evaluate the system’s effectiveness, a user study was conducted with regular visitors at the Hecht Museum. The results showed that the algorithm successfully and quickly identified POIs in 97.6% of cases. Additionally, participants completed the System Usability Scale (SUS) and provided open-ended feedback, indicating high satisfaction with the system’s accuracy and speed while also offering suggestions for future improvements. Full article

Background/Objectives: Premature ovarian insufficiency (POI) is a leading cause of hypoestrogenism in women under the age of 40 years and is associated with an increased risk of impaired bone health. Early diagnosis and timely hormonal intervention are essential to prevent irreversible bone loss. However, diagnostic delay is not uncommon in clinical practice. Methods: We conducted a retrospective analysis of 168 women diagnosed with POI or early menopause (EM) between 2017 and 2024 at a tertiary gynecological endocrinology unit. Bone mineral density (BMD) and T-score were assessed by dual-energy X-ray absorptiometry (DXA) at the time of diagnosis in 125 patients, of whom 116 had secondary amenorrhea. The interval between the last menstrual period (LMP) and diagnosis was used to assess the impact of diagnostic delay. The patients were further stratified by serum estradiol (E2) levels and body mass index (BMI). Results: At the time of diagnosis, 43.1% of patients had osteopenia, and 10.3% had osteoporosis. A statistically significant negative correlation was observed between time to diagnosis and BMD (r = −0.225, p = 0.022), with a similar trend seen for T-score (r = −0.211, p = 0.031). In patients with E2 ≤ 5 ng/L, the association was stronger (BMD: r = −0.401, p = 0.026). Lower E2 levels tended to be associated with poorer bone health in women with a BMI < 25 kg/m², whereas no such trend was observed in those with a higher BMI. Conclusions: Our findings indicate that diagnostic delay in POI is associated with deterioration in bone health, particularly in lean patients and those with severe hypoestrogenism. These results underscore the importance of early recognition and timely initiation of hormone therapy to preserve bone mass and reduce long-term skeletal complications. Full article

Recognition and detection of building groups are core tasks in cartographic research. Current recognition methods that rely on spatial and geometric features often neglect semantic aspects, failing to account for the complex relationships between buildings and their real-world semantic associations. This limitation hampers the ability to fully capture human understanding of the real world. Based on this, this paper proposes a novel method for building group recognition that integrates both spatial geometric and semantic features. The method effectively identifies building group structures by considering spatial proximity, geometry, and semantic similarity. First, spatial proximity between buildings is defined by constructing a neighborhood graph based on Delaunay triangulation, and the spatial geometric features of each building are extracted. The spatial distance and semantic intensity relationships between Point of Interest (POI) data and buildings are used for semantic feature extraction. Subsequently, a spatial–semantic dual clustering strategy is applied in two stages to aggregate the buildings and generate preliminary grouping results. Finally, the grouping results are refined through an optimal graph segmentation strategy, which ensures both global and local optimization. The proposed method is applied to two areas in Shenzhen City, China, and the experimental results demonstrate that, compared with other methods, it more effectively identifies building groups with coherent spatial, geometric, and semantic features, improving the Adjusted Rand Index (ARI) from 0.589 to 0.701. This approach provides significant support for intelligent map generalization and personalized knowledge services in the era of big data. Full article

Urban Functional Zones (UFZs) are spatial units of the city divided according to specific functional activities. Detailed identification of UFZs is vital for optimizing urban management, guiding planning and design, and promoting sustainable development. However, existing UFZ recognition methods face significant challenges, such as difficulties in effectively integrating multi-source heterogeneous data, capturing dynamic spatiotemporal patterns, and addressing the complex interrelationships among various data types. These issues significantly limit the applicability of UFZ mapping in complex urban scenarios. To address these challenges, this paper proposes a tripartite neural network (TriNet) for multimodal data processing, including Remote Sensing (RS) images, Point of Interest (POI) data, and Origin–Destination (OD) data, fully utilizing the complementarity of different data types. TriNet comprises three specialized branches: ImgNet for spatial features extraction from images, POINet for functional density distribution features extraction from POI data, and TrajNet for spatiotemporal pattern features extraction from OD data. Finally, the method deeply fuses these features through a feature fusion module, which utilizes a two-layer fully connected network for deep fusion, allowing the model to fully utilize the interdependencies among the data types, significantly improving the UFZ classification accuracy. The experimental data are generated by mapping OpenStreetMap (OSM) vector into conceptual representations, integrating images with social sensing data to create a comprehensive UFZ classification benchmark. The method achieved an overall accuracy of 84.13% on the test set of Chongqing’s main urban area, demonstrating high accuracy and robustness in UFZ classification tasks. The experimental results show that the TriNet model performs effectively in UFZ classification. Full article

Urban functional zones (UFZs) are related to people’s daily activities. Accurate recognition of UFZs is of great significance for an in-depth understanding of the complex urban system and optimizing the urban spatial structure. Emerging geospatial big data provide new ideas for humans to recognize urban functional zones. Point-of-interest (POI) data have achieved good results in the recognition of UFZs. However, since humans are the actual users of urban functions, and POI data only reflect static socioeconomic characteristics without considering the semantic and temporal features of dynamic human activities, it leads to an incomplete and insufficient representation of complex UFZs. To solve these problems, we proposed a data-synthesis-driven approach to quantify and analyze the distribution and mixing of urban functional zones. Firstly, representation learning is used to mine the spatial semantic features, activity temporal features, and activity semantic features that are embedded in POI data and social media check-in data from spatial, temporal, and semantic aspects. Secondly, a weighted Stacking ensemble model is used to fully integrate the advantages between different features and classifiers to infer the proportions of urban functions and dominant functions of each urban functional zone. A case study within the 5th Ring Road of Beijing, China, is used to evaluate the proposed method. The results show that the approach combining dynamic and static features of POI data and social media data effectively represents the semantic information of UFZs, thereby further improving the accuracy of UFZ recognition. This work can provide a reference for uncovering the hidden linkages between human activity characteristics and urban functions. Full article

Robust autonomous navigation systems rely on mapping, locomotion, path planning, and localization factors. Localization, one of the most essential factors of navigation, is a crucial requirement for a mobile robot because it needs the capability to localize itself in the environment. Global Positioning Systems (GPSs) are commonly used for outdoor mobile robot localization tasks. However, various environmental circumstances, such as high-rise buildings and trees, affect GPS signal quality, which leads to reduced precision or complete signal blockage. This study proposes a visual-based localization system for outdoor mobile robots in crowded urban environments. The proposed system comprises three steps. The first step is to detect the text in urban areas using the “Efficient and Accurate Scene Text Detector (EAST)” algorithm. Then, EasyOCR was applied to the detected text for the recognition phase to extract text from images that were obtained from EAST. The results from text detection and recognition algorithms were enhanced by applying post-processing and word similarity algorithms. In the second step, once the text detection and recognition process is completed, the recognized word (label/tag) is sent to the Places API in order to return the recognized word’s coordinates that are passed within the specified radius. Parallely, points of interest (POI) data are collected for a defined area by a certain radius while the robot has an accurate internet connection. The proposed system was tested in three distinct urban areas by creating five scenarios under different lighting conditions, such as morning and evening, using the outdoor delivery robot utilized in this study. In the case studies, it has been shown that the proposed system provides a low error of around 4 m for localization tasks. Compared to existing works, the proposed system consistently outperforms all other approaches using just one sensor. The results indicate the efficacy of the proposed system for localization tasks in environments where GPS signals are limited or completely blocked. Full article

Effective production, living, and ecological space allocation is essential for improving and optimizing urban space development. In this study, we proposed a production–living–ecological space (PLES) identification method based on Point of Interest (POI) data and China Land Cover Dataset (CLCD) to identify PLESs in Xuzhou City for the years 2012, 2018, and 2022, with an average recognition accuracy of 89.81%. Moreover, the land-use transfer matrix, center of gravity migration, and Geo-detector were used to reveal the spatiotemporal pattern evolution of PLESs. The results showed that: (1) The distribution of PLESs presented significant differentiation between Urban Built-Up Area (UBUA) and Non-Urban Built-Up Area (NUBUA). UBUA was mainly composed of living spaces, while NUBUA was primarily characterized by production–ecological spaces. (2) The intensive utilization of urban land led to an increase in the area of multifunctional spaces, while the complexity of urban space increased. (3) During 2012 to 2022, the center of gravity of PLESs remained relatively stable. The moving distances were all less than 1 km (except for ecological space from 2012 to 2018). (4) The evolution of PLESs was closely linked with socio-economic factors, and the interactions between the factors also had a significant driving effect on PLESs. Full article

Prior studies have failed to adequately address intangible characteristics and lacked a comprehensive quantification of cultural dimensions. Additionally, such works have not merged supervised and unsupervised classification methodologies. To address these gaps, this study employed multidimensional numerical techniques for precise spatial pattern recognition and urban morphology classification at the block scale. By examining building density, mean floor numbers, functional compositions, and street block mixed-use intensities, alongside historical and contemporary cultural assets within blocks—with assigned weights and entropy calculations from road networks, building vectors, and POI data—a hierarchical categorization of high, medium, and low groups was established. As a consequence, cluster analysis revealed seven distinctive morphology classifications within the studied area, each with unique spatial configurations and evolutionary tendencies. Key findings include the dominance of high-density, mixed-use blocks in the urban core, the persistence of historical morphologies in certain areas, and the emergence of new, high-rise clusters in recently developed zones. The investigation further elucidated the spatial configurations and evolutionary tendencies of each morphology category. These insights lay the groundwork for forthcoming studies to devise morphology-specific management strategies, thereby advancing towards a more scientifically grounded, rational, and precision-focused approach to urban morphology governance. Full article

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations. Full article

For high-precision navigation, obtaining and maintaining high-precision point-of-interest (POI) data on the road network is crucial. In urban areas with complex terrains, the accuracy of traditional road network POI acquisition methods often falls short. To address this issue, we introduce DualNet-PoiD, a hybrid neural network designed for the efficient recognition of road network POIs in intricate urban environments. This method leverages multimodal sensory data, incorporating both vehicle trajectories and remote sensing imagery. Through an enhanced dual-attention dilated link network (DAD-LinkNet) based on ResNet18, the system extracts static geometric features of roads from remote sensing images. Concurrently, an improved gated recirculation unit (GRU) captures dynamic traffic characteristics implied by vehicle trajectories. The integration of a fully connected layer (FC) enables the high-precision identification of various POIs, including traffic light intersections, gas stations, parking lots, and tunnels. To validate the efficacy of DualNet-PoiD, we collected 500 remote sensing images and 50,000 taxi trajectory data samples covering road POIs in the central urban area of the mountainous city of Chongqing. Through comprehensive area comparison experiments, DualNet-PoiD demonstrated a high recognition accuracy of 91.30%, performing robustly even under conditions of complex occlusion. This confirms the network’s capability to significantly improve POI detection in challenging urban settings. Full article

Research on the identification of urban functional regions is of great significance for the understanding of urban structure, spatial planning, resource allocation, and promoting sustainable urban development. However, achieving high-precision urban functional region recognition has always been a research challenge in this field. For this purpose, this paper proposes an urban functional region identification method called ASOE (activity–scene–object–economy), which integrates the features from multi-source data to perceive the spatial differentiation of urban human and geographic elements. First, we utilize VGG16 (Visual Geometry Group 16) to extract high-level semantic features from the remote sensing images with 1.2 m spatial resolution. Then, using scraped building footprints, we extract building object features such as area, perimeter, and structural ratios. Socioeconomic features and population activity features are extracted from Point of Interest (POI) and Weibo data, respectively. Finally, integrating the aforementioned features and using the Random Forest method for classification, the identification results of urban functional regions in the main urban area of Xi’an are obtained. After comparing with the actual land use map, our method achieves an identification accuracy of 91.74%, which is higher than other comparative methods, making it effectively identify four typical urban functional regions in the main urban area of Xi’an (e.g., residential regions, industrial regions, commercial regions, and public regions). The research indicates that the method of fusing multi-source data can fully leverage the advantages of big data, achieving high-precision identification of urban functional regions. Full article

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented. Full article

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi‘an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management. Full article

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents. Full article

Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ extraction. However, many existing methods only use a single type of data or simply combine the two, failing to take full advantage of the complementary advantages between them. Therefore, we designed a deep-learning framework that integrates the above two types of data to identify urban functional areas. In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. Specifically, we extract visual features from RSI data, while POI data are converted into a distance heatmap tensor that is input into the CNN with gated attention mechanisms to extract social features. Then, we use a feature fusion module (FFM) with adaptive weights to fuse the two types of features. The second part is the spatial-relationship-modeling module. We designed a new spatial-relationship-learning network based on a vision transformer model with long- and short-distance attention, which can simultaneously learn the global and local spatial relationships of the urban functional zones. Finally, a feature aggregation module (FGM) utilizes the two spatial relationships efficiently. The experimental results show that the proposed model can fully extract visual features, social features, and spatial relationship features from RSIs and POIs for more accurate UFZ recognition. Full article