Search Results (43)

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

The contemporary digital landscape overwhelms visitors with fragmented and dynamic information, complicating travel planning and often leading to decision paralysis. This paper presents a real-world case study on the design and deployment of an intelligent tourism assistant for Valencia, Spain, built upon a Retrieval-Augmented Generation (RAG) architecture. To address the complexity of integrating static attraction data, live events, and geospatial context, we implemented a multi-agent system orchestrated via the ReAct (Reason + Act) paradigm, comprising specialized Retrieval, Events, and Geospatial Agents. Powered by a large language model, the system unifies heterogeneous data sources—including official tourism repositories and OpenStreetMap—within a single conversational interface. Our contribution centers on practical insights and engineering lessons from developing RAG in an operational urban tourism environment. We outline data preprocessing strategies, such as coreference resolution, to improve contextual consistency and reduce hallucinations. System performance is evaluated using Retrieval Augmented Generation Assessment (RAGAS) metrics, yielding quantitative results that assess both retrieval efficiency and generation quality, with the Mistral Small 3.1 model achieving an Answer Relevancy score of 0.897. Overall, this work highlights both the challenges and advantages of using agent-based RAG to manage urban-scale information complexity, providing guidance for developers aiming to build trustworthy, context-aware AI systems for smart destination management. Full article

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

This study presents a system model developed for collecting and analyzing spatial data on the project environment of transport infrastructure development in the post-war context, with a focus on supporting sustainable management and recovery planning. The model utilizes the OpenStreetMap Overpass Application Programming Interface (Overpass API) to extract structured geospatial information from OpenStreetMap (OSM), enabling efficient and accurate assessments of settlements affected by armed conflict. Python 3.11-based software modules were created to process OSM data, evaluate 17 relevant attributes of transport infrastructure objects, and visualize key characteristics for decision-makers. A case study was conducted on 23 Ukrainian settlements with partially damaged infrastructure, demonstrating how the proposed model facilitates timely and informed decisions for infrastructure redevelopment. By improving the accessibility and quality of spatial data, the model enhances the capacity for sustainable management of post-war transport infrastructure projects. To ensure the quality of spatial data obtained from OSM, a verification procedure was carried out by cross-checking with satellite images and official national geospatial data. The results showed an average deviation of ±4.4% in the length of road sections, confirming the reliability and accuracy of spatial objects obtained from OSM for use in transport infrastructure planning. The findings offer valuable insights for regional planners, public administrators, and policymakers involved in sustainable reconstruction and digital governance. Future research will focus on developing a comprehensive information system for identifying and prioritizing infrastructure development projects within defined administrative units such as municipalities and local communities. Full article

In remote sensing building detection tasks, data acquisition remains a critical bottleneck that limits both model performance and large-scale deployment. Due to the high cost of manual annotation, limited geographic coverage, and constraints of image acquisition conditions, obtaining large-scale, high-quality labeled datasets remains a significant challenge. To address this issue, this study proposes an automatic semantic labeling framework for remote sensing imagery. The framework leverages geospatial vector data provided by OpenStreetMap, precisely aligns it with high-resolution satellite imagery from Bing Maps through projection transformation, and incorporates a quality-aware sample filtering strategy to automatically generate accurate annotations for building detection. The resulting dataset comprises 36,647 samples, covering buildings in both urban and suburban areas across multiple cities. To evaluate its effectiveness, we selected three publicly available datasets—WHU, INRIA, and DZU—and conducted three types of experiments using the following four representative object detection models: SSD, Faster R-CNN, DETR, and YOLOv11s. The experiments include benchmark performance evaluation, input perturbation robustness testing, and cross-dataset generalization analysis. Results show that our dataset achieved a mAP at 0.5 intersection over union of up to 93.2%, with a precision of 89.4% and a recall of 90.6%, outperforming the open-source benchmarks across all four models. Furthermore, when simulating real-world noise in satellite image acquisition—such as motion blur and brightness variation—our dataset maintained a mean average precision of 90.4% under the most severe perturbation, indicating strong robustness. In addition, it demonstrated superior cross-dataset stability compared to the benchmarks. Finally, comparative experiments conducted on public test areas further validated the effectiveness and reliability of the proposed annotation framework. Full article

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solution for precise road intersection mapping that eliminates common digitization errors. Unlike conventional approaches that only detect intersection positions, our method systematically reconstructs complete intersection geometries while maintaining topological consistency. The technique combines plane surveying principles (including line-bearing analysis and curve detection) with spatial analytics to automatically identify intersections, characterize their connectivity patterns, and assign unique identifiers based on configurable parameters. When evaluated across multiple urban contexts using diverse data sources (manual digitization and OpenStreetMap), the method demonstrated consistent performance with mean Intersection over Union greater than 0.85 and F-scores more than 0.91. The high correctness and completeness metrics (both more than 0.9) confirm its ability to minimize both false positive and omission errors, even in complex roadway configurations. The approach consistently produced gap-free, overlap-free outputs, showing strength in handling interchange geometries. The solution enables transportation agencies to make data-driven maintenance decisions by providing reliable, standardized intersection inventories. Its adaptability to varying input data quality makes it particularly valuable for large-scale infrastructure monitoring and smart city applications. Full article

Green open space in high-density cities has positive significance in terms of improving the quality of the living environment and solving problems such as “urban diseases”. Taking the high-density urban districts of the Macau Peninsula and Monaco as examples, this study divides the planning index of open space green quantity into two dimensions: the blue-green spaces occupancy rate (BGOR) within urban land areas and the blue-green spaces visibility rate (BGVR) of the main streetscape. Using satellite remote-sensing maps, GIS databases, and street-view images, this study evaluates the current green quantity in both regions and compares them to identify best practices. This study aims to assess and enhance the green quantity found in the open spaces of high-density cities, using the Macau Peninsula and Monaco as case studies. The primary research questions are as follows: (1) How can the green quantity in open spaces be effectively measured in high-density urban environments? (2) What planning strategies can be implemented to increase the green quantity and improve the urban living environment in such areas? Therefore, this study proposes planning strategies such as three-dimensional greening, converting grey spaces to green spaces, and implementing policies to encourage public participation in greening efforts. These strategies aim to enhance the green quantity in open spaces, thereby improving the urban living environment in high-density cities like Macau and providing a reference for similar urban areas in the world. Full article

Although there are many articles dealing with web map applications, they often focus on just one or a few applications. Several articles deal with the technical solution of the applications, but relatively few are focused on the cartographic aspects of these applications. This article evaluates eight web mapping applications based on six cartographic aspects: map key, map scale, map layout, navigation elements, labels, and analytical tools. The objective is to identify differences in the presentation of geographic information and propose improvements for cartographic quality and user-friendliness. The methodology involved visual analysis at two scales. The comparison included applications such as Mapy.cz, OpenStreetMap, Google Maps, Bing Maps, HERE Maps, MapQuest, ViaMichelin, and Locus Map. The results revealed significant differences among the applications that may impact user orientation and experience. For instance, Google Maps does not display forest symbols on its default map, which can reduce clarity, whereas Mapy.cz offers the most comprehensive range of analytical tools. Advertisements in applications like MapQuest and ViaMichelin disrupt the user experience, and some applications lack essential functions, such as distance measurement. The paper identifies strengths and weaknesses in the cartographic design of these applications. Findings reveal that while each application possesses unique characteristics, they share common features. An interesting feature is the absence of cartographic symbols and labels of some elements in some applications. The study recommends the unification of cartographic principles and further user testing to optimize the layout and functionality of web mapping applications. Full article

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage. Full article

The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study’s results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility. Full article

Georeferenced data are crucial for addressing societal spatial challenges, as most corporate and governmental information is location-compatible. However, many open-source solutions lack automation in geocoding while ensuring quality. This study evaluates the functionalities of various R packages and their integration with external APIs for converting postal addresses into geographic coordinates. Among the fifteen R methods/packages reviewed, tidygeocoder stands out for its versatility, though discrepancies in processing times and missing values vary by provider. The accuracy was assessed by proximity to original dataset coordinates (Madrid street map) using a sample of 15,000 addresses. The results indicate significant variability in performance: MapQuest was the fastest, ArcGIS the most accurate, and Nominatim had the highest number of missing values. To address these issues, an alternative web scraping methodology is proposed, substantially reducing the error rates and missing values, but raising potential legal concerns. This comparative analysis highlights the strengths and limitations of different geocoding tools, facilitating better integration of geographic information into datasets for researchers and social agents. Full article

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces. Full article

Urban street greening, a key component of urban green spaces, significantly impacts residents’ physical and mental well-being, contributing substantially to the overall quality and welfare of urban environments. This paper presents a novel framework that integrates street greenery with accessibility, enabling a detailed evaluation of the daily street-level greenery visible to residents. This pioneering approach introduces a new measurement methodology to quantify the quality of urban street greening, providing robust empirical evidence to support its enhancement. This study delves into Nanjing’s five districts, employing advanced image semantic segmentation based on machine learning techniques to segment and extract green vegetation from Baidu Street View (BSV) images. Leveraging spatial syntax, it analyzes street network data sourced from OpenStreetMap (OSM) to quantify the accessibility values of individual streets. Subsequent overlay analyses uncover areas characterized by high accessibility but inadequate street greening, underscoring the pressing need for street greening enhancements in highly accessible zones, thereby providing valuable decision-making support for urban planners. Key findings revealed that (1) the green view index (GVI) of sampled points within the study area ranged from 15.79% to 38.17%, with notably better street greening conditions observed in the Xuanwu District; (2) the Yuhua District exhibited comparatively lower pedestrian and commuting accessibility than the Xuanwu District; and (3) approximately 139.62 km of roads in the study area demonstrated good accessibility but lacked sufficient greenery visibility, necessitating immediate improvements in their green landscapes. This research utilizes the potential of novel data and methodologies, along with their practical applications in planning and design practices. Notably, this study integrates street greenery visibility with accessibility to explore, from a human-centered perspective, the tangible benefits of green landscapes. These insights highlight the opportunity for local governments to advance urban planning and design by implementing more human-centered green space policies, ultimately promoting societal equity. Full article

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts. Full article