Search Results (141)

In pursuit of the Sustainable Development Goals (SDGs)’s objective of eliminating slum cities, the government of Indonesia has initiated a survey-based slum mapping program. Unfortunately, recent observations have highlighted considerable inconsistencies in the mapping process. These inconsistencies can be attributed to various factors, including variations in the expertise of surveyors and the intricacies of the indicators employed to characterize slum conditions. Consequently, reliable data is lacking, which poses a significant barrier to effective monitoring of slum upgrading programs. Remote sensing (RS)-based approaches, particularly those employing deep learning (DL) techniques, have emerged as a highly effective and accurate method for identifying slum areas. However, the reliance on RS alone is likely to encounter challenges in complex urban environments. A substantial body of research has previously identified the merits of integrating land surface data with RS. Therefore, this study seeks to combine remote sensing imagery (RSI) with street view imagery (SVI) for the purpose of slum mapping and compare its accuracy with a field survey conducted in 2024. The city of Bandung is a pertinent case study, as it is facing a considerable increase in population density. These slums collectively encompass approximately one-tenth of Bandung City’s population as of 2020. The present investigation evaluates the mapping results obtained from four distinct deep learning (DL) networks: The first category comprises FCN, which utilizes RSI exclusively, and FCN-DK, which also employs RSI as its sole input. The second category consists of two networks that integrate RSI and SVI, namely FCN and FCN-DK. The findings indicate that the integration of RSI and SVI enhances the precision of slum mapping in Bandung City, particularly when employing the FCN-DK network, achieving an accuracy of 86.25%. The results of the mapping process employing a combination of the FCN-DK network, which utilizes the RSI and SVI, indicate the presence of 2294 light slum points and 29 medium slum points. It should be noted that the outcomes are contingent upon the methodological approach employed, the accessibility of the dataset, and the training data that mirrors the distribution of slums in 2020 and the specific degree of its integration within the FCN network. The FCN-DK model, which integrates RSI and SVI, demonstrates enhanced performance in comparison to the other models examined in this study. Full article

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The research introduces novel geoprocessing tools-based GIS techniques that mathematically simulate cyclists’ angles of view and the distances to nearby environmental features. It provides precise insights into some potential hazards and infrastructure challenges encountered while cycling. This research focuses on managing and analysing the data collected, utilising OpenStreetMap (OSM) as vector-based supporting data. It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. The analysis is categorised into specific classes to evaluate the impacts of these aspects of the environment on cyclists’ behaviours. The current investigation highlights the importance of integrating the objective environmental elements surrounding the route with subjective perceptions and then determining the influence of these environmental elements on cyclists’ behaviours. Unlike previous studies that ignore cyclists’ visual perspectives in the context of real-world data, this work integrates objective GIS data with cyclists’ field of view-based modelling to identify high-risk areas and highlight the need for enhanced safety measures. The proposed approach equips urban planners and designers with data-informed strategies for creating safer cycling infrastructure, fostering sustainable mobility, and mitigating urban congestion. Full article

The world is characterised by the growing volumes and flow of goods, which, amid benefits to economic development, result in negative externalities affecting the sustainability of cities. Although numerous studies have analysed the locational patterns of logistics facilities in cities, further research is required to examine their real estate patterns and trends. The aim of the paper is, therefore, to analyse the value of land accommodating logistics facilities in the City of Cape Town municipality, South Africa. Given the lack of dedicated geo-spatial data, logistics firms were searched on Google Maps, utilising a combination of aerial photography and street view imagery. Three main attributes of land parcels hosting logistics facilities were thereafter captured from the municipal cadastral information: property extent, street address, and property number. The latter two were used to extract the 2018 and 2022 property market values from the valuation rolls on the municipal website, followed by statistical, spatial, and geographically weighted regression (GWR) analyses. Zones near the central business district and seaport, as well as areas with prime road-based accessibility, had high market values, while those near the railway stations did not stand out. However, GWR yielded weak relationships between market values and the locational variables analysed, arguably showing a disconnect between spatial planning and logistics planning. Towards augmenting sustainable logistics, it is recommended that relevant stakeholders strategically integrate logistics into spatial planning, and particularly revitalise freight rail to attract investment to logistics hubs with direct railway access. Full article

Exposure modeling plays a crucial role in disaster risk assessments by providing geospatial information about assets at risk and their characteristics. Detailed exposure data enhances the spatial representation of a rapidly changing environment, enabling decision-makers to develop effective policies for reducing disaster risk. This work proposes and demonstrates a methodology linking volunteered geographic information from OpenStreetMap (OSM), street-level imagery from Google Street View (GSV), and deep learning object detection models into the automated creation of exposure datasets for power grid transmission towers, assets particularly vulnerable to strong wind, and other perils. Specifically, the methodology is implemented through a start-to-end pipeline that starts from the locations of transmission towers derived from OSM data to obtain GSV images capturing the towers in a given region, based on which their relevant features for risk assessment purposes are determined using two families of object detection models, i.e., single-stage and two-stage detectors. Both models adopted herein, You Only Look Once version 5 (YOLOv5) and Detectron2, achieved high values of mean average precision (mAP) for the identification task (83.67% and 88.64%, respectively), while Detectron2 was found to outperform YOLOv5 for the classification task with a mAP of 64.89% against a 50.62% of the single-stage detector. When applied to a pilot study area in northern Portugal comprising approximately 5.800 towers, the two-stage detector also exhibited higher confidence in its detection on a larger part of the study area, highlighting the potential of the approach for large-scale exposure modeling of transmission towers. Full article

This study proposes a practical integration of an existing deep learning model (YOLOv9-E) and social participation GIS using multi-source remote sensing data to identify asbestos-containing materials located on the side of a building affected by light occlusions. These objects are often undetectable by traditional vertical or oblique photogrammetry, yet their precise localization is essential for effective removal planning. By leveraging the mobility and responsiveness of citizen investigators, we conducted fine-grained surveys in community spaces that were often inaccessible using conventional methods. The YOLOv9-E model demonstrated robustness on mobile-captured images, enriched with geolocation and orientation metadata, which improved the association between detections and specific buildings. By comparing results from Google Street View and field-based social imagery, we highlight the complementary strengths of both sources. Rather than introducing new algorithms, this study focuses on an applied integration framework to improve detection coverage, spatial precision, and participatory monitoring for environmental risk management. The dataset comprised 20,889 images, with 98% being used for training and validation and 2% being used for independent testing. The YOLOv9-E model achieved an mAP50 of 0.81 and an F1-score of 0.85 on the test set. Full article

Islands represent distinctive geographical landscapes where cultural heritage, history, and ecological systems converge, offering critical insights into human–environment interactions. This study investigates how visual storytelling through digital tools such as the Historical Environment Map Viewer, Environment Digimap, Google Maps and Google Street View, and ArcGIS Field Maps can be employed to capture, interpret, and communicate islands’ landscape changes. By integrating historical environmental mapping, landscape change mapping, street map views, and field observations, this study creates a layered visual narrative that reveals shifts in land use, settlement patterns, and ecological features over time. Rathlin Island represents a distinctive island landscape, and this study uses visual storytelling as a tool to foster a broader public understanding of environmental conservation and engagement with the island’s ecologial challenges. The study demonstrates that multi-perspective, interdisciplinary methods provide valuable insights into the complex dynamics of landscape change, while also offering a comprehensive vision of sustainable future landscape on small islands. Full article

The strategic prioritization of inefficient industrial land (IIL) redevelopment is critical for directing capital allocation toward sustainable urban regeneration. However, current redevelopment prioritization suffers from inefficient identification of IIL and ambiguous characterization of redevelopment potential, which hinders the efficiency of land resource allocation. To address these challenges, this study develops an AI-driven redevelopment prioritization framework for identifying IIL, evaluating redevelopment potential, and establishing implementation priorities. For land identification we propose an improved YOLOv11 model with an AdditiveBlock module to enhance feature extraction in complex street view scenes, achieving an 80.1% mAP on a self-built dataset of abandoned industrial buildings. On this basis, a redevelopment potential evaluation index system is constructed based on the necessity, maturity, and urgency of redevelopment, and the Particle Swarm Optimization-Projection Pursuit (PSO-PP) model is introduced to objectively evaluate redevelopment potential by adaptively reducing the reliance on expert judgment. Subsequently, the redevelopment priorities were classified according to the calculated potential values. The proposed framework is empirically tested in the central urban area of Ningbo City, China, where inefficient industrial land is successfully identified and redevelopment priority is categorized into near-term, medium-term, and long-term stages. Results show that the framework integrating computer vision and machine learning technology can effectively provide decision support for the redevelopment of IIL and offer a new method for promoting the smart growth of urban space. Full article

With the growing deployment of autonomous driving and unmanned systems in road environments, efficiently and accurately performing environmental perception and map construction has become a significant challenge for SLAM systems. In this paper, we propose an innovative SLAM framework comprising a frontend tracking network called SGF-net and a backend filtering mechanism, namely Semantic Gaussian Filter. This framework effectively suppresses dynamic objects by integrating feature point detection and semantic segmentation networks, filtering out Gaussian point clouds that degrade mapping quality, thus enhancing system performance in complex outdoor scenarios. The inference speed of SGF-net has been improved by over 23% compared to non-fused networks. Specifically, we introduce SGF-SLAM (Semantic Gaussian Filter SLAM), a dynamic mapping framework that shields dynamic objects undergoing temporal changes through multi-view geometry and semantic segmentation, ensuring both accuracy and stability in mapping results. Compared with existing methods, our approach can efficiently eliminate pedestrians and vehicles on the street, restoring an unobstructed road environment. Furthermore, we present a map update function, which is aimed at updating areas occluded by dynamic objects by using semantic information. Experiments demonstrate that the proposed method significantly enhances the reliability and adaptability of SLAM systems in road environments. Full article

Historic cities face a dual challenge of preserving cultural authenticity and adapting to modern urbanization, yet existing studies often overlook the multidimensional coupling mechanisms critical for sustainable urban renewal. This research has proposed a replicable framework to balance heritage conservation, ecological restoration, and pedestrian mobility. Focusing on the historic city of Shenyang, this study evaluated spatial dynamics via the Walkability Index (WI), Green View Index (GVI), and Cultural Heritage Index (CHI), and quantified their coupling coordination patterns. Multisource datasets including OpenStreetMap road networks, POIs, and Baidu street-view imagery were integrated. A Coupling Coordination Degree (CCD) model was developed to assess system interactions. Results revealed moderate overall walkability (WI = 42.66) with stark regional disparities, critically low greening (GVI = 10.14%), and polarized heritage distribution (CHI = 18.73) in Shenyang historic city. Tri-system coupling was moderate (CCD = 0.409–0.608), constrained by green-heritage disconnects in key districts. This work could contribute to interdisciplinary discourse by bridging computational modeling with human-centric urban design, providing scalable insights for global historic cities. Full article

The style and overall urban texture of historic districts embody rich social and cultural values. Therefore, how to make relevant environmental elements effectively perceived visually has become the key to protecting and displaying historic streets. Based on this, the non-subjective eye movement data and subjective impression of the subject were collected through an eye-tracking experiment and semi-structured interview. ErgoLAB was used to generate eye-tracking metrics and heat maps based on eye movement data, and ROST-CM6 software was used to generate word frequency and emotional degree data for interview text. Through comparative analysis, it is found that the subjective and objective evaluation indexes of the subjects tend to be consistent in general, but the visual behavior characteristics of different environmental elements’ types are different. The greater the variety of elements involved in visual perception, the longer the time required for participants to identify the relevant elements. The extent of element distribution also influenced differences in visual perception. Additionally, visual perceptions from partial elevation views and overall human perspective angles were largely similar, with distinctive elements attracting more interest. This study has an exploratory nature, and its findings contribute to the preservation and enhancement of the visual quality of historic districts. Full article

Streetscapes in old urban areas are not only an important carrier to show regional economies and city style, but also closely correlate to urban residents’ everyday life and the hustle and bustle in which they live. Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. Furthermore, the impact of spontaneous streetscape features on diversified social sensing has rarely been examined. This paper combined the mobile collection of street view images (SVIs) and a machine learning algorithm to calculate eight types of spontaneous streetscape elements and integrated two online platforms (Dianping and Sina Weibo) to map the distribution of economic vitality and social media perception, respectively. Then, through comparing multiple regression models, the impacts of the spontaneous streetscape characteristics on social sensing were revealed. The results include the following two aspects: (1) overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity; and (2) specifically, the spontaneous streetscape elements can be divided into three categories, given the differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results. For example, proper use of open-interface storefronts, ads, and banners is consistent with the common suggestions, while the excessive pursuit of interface diversity and the use of cultural elements may bring an ambiguous effect. This paper provides a transferable analytical framework for mixed and data-driven sensing of streetscape regeneration and can potentially inspire related decisionmakers to adopt a more refined and low-cost approach to enhance urban vitality and sustainability. Full article

This study explores a methodology for assessing territorial innovation potential using OpenStreetMap (OSM) data and geoinformation technologies. Traditional assessment methods often rely on aggregated statistical data, which provide a generalized view but overlook the spatial heterogeneity within regions. To address this limitation, the proposed methodology utilizes open, up-to-date OSM data to identify key infrastructure elements, such as universities, research institutions, and data centers, which drive regional innovation. The methodology includes data extraction, harmonization, and spatial analysis using tools like QGIS and kernel density estimation. Results from the PoliRuralPlus project pilot regions highlight significant differences in innovation potential between urban centers and rural areas, emphasizing the importance of detailed spatial data in policy making and regional development planning. The study concludes that OSM-based assessments provide spatially detailed targeted, flexible, and replicable insights into regional innovation potential compared to traditional methods. However, the limitations of crowdsourced data, such as variability in quality and completeness, are acknowledged. Future developments aim to integrate OSM with official statistical data and other data resources to support more efficient and fair resource allocation and strategic investments in regional innovation ecosystems. Full article

An increasing trend towards the installation of photovoltaic (PV) solar energy generation capacity is driven by several factors including the desire for greater energy independence and, especially, the desire to decarbonize industrial economies. While large ‘solar farms’ can be installed in relatively open areas, urban environments also offer scope for significant energy generation, although the heterogeneous nature of the surface of the urban fabric complicates the task of forming an area-wide view of this potential. In this study, we investigate the potential offered by publicly available airborne LiDAR data, augmented using data from OpenStreetMap (OSM), to estimate rooftop PV generation capacities from individual buildings and regionalized across an entire small city. We focus on the island of Tromsøya in the city of Tromsø, Norway, which is located north (69.6° N) of the Arctic Circle, covers about 13.8 km², and has a population of approximately 42,800. A total of 16,377 buildings were analyzed. Local PV generation potential was estimated between 120 and 180 kWh m⁻² per year for suitable roof areas, with a total estimated generation potential of approximately 200 GWh per year, or approximately 30% of the city’s current total consumption. Regional averages within the city show significant variations in potential energy generation, highlighting the importance of roof orientation and building density, and suggesting that rooftop PV could play a much more substantial role in local energy supply than is commonly assumed at such high latitudes. The analysis method developed here is rapid, relatively simple, and easily adaptable to other locations. Full article

Existing methods for detecting road damage mainly depend on manual inspections or sensor-equipped vehicles, which are inefficient, have limited coverage, and are susceptible to errors and delays. These traditional methods also struggle with detecting minor damage, such as small cracks and initial potholes, making real-time road monitoring challenging. To address these issues and improve the performance for real-time road damage detection using Street View Image Data (SVRDD), this study propose DAPONet, a new deep learning model. DAPONet proposes three main innovations: (1) a dual attention mechanism that combines global context and local attention, (2) a multi-scale partial overparameterization module (CPDA), and (3) an efficient downsampling module (MCD). Experimental results on the SVRDD public dataset show that DAPONet reaches a mAP50 of 70.1%, surpassing YOLOv10n (an optimized version of YOLO) by 10.4%, while reducing the model’s size to 1.6 M parameters and cutting FLOPs to 1.7 G, resulting in a 41% and 80% decrease, respectively. Furthermore, the model’s mAP50-95 of 33.4% on the MS COCO2017 dataset demonstrates its superior performance, with a 0.8% improvement over EfficientDet-D1, while reducing parameters and FLOPs by 74%. Full article

This study introduces a new framework for combining calibrated mobile mapping system (MMS) data and low-cost unmanned aerial vehicle (UAV) images to generate seamless, high-fidelity 3D urban maps. This approach addresses the limitations of single-source mapping, such as occlusions in aerial top views and insufficient vertical detail in ground-level data, by utilizing the complementary strengths of the two technologies. The proposed approach combines cloth simulation filtering for ground point extraction from MMS data with deep-learning-based segmentation (U²-Net) for feature extraction from UAV images. Street-view MMS images are projected onto a top-down viewpoint using inverse perspective mapping to align diverse datasets, and precise cross-view alignment is achieved using the LightGlue technique. The spatial accuracy of the 3D model was improved by integrating the matched features as ground control points into a structure from the motion pipeline. Validation using data from the campus of Yonsei University and the nearby urban area of Yeonhui-dong yielded notable accuracy gains and a root mean square error of 0.131 m. Geospatial analysis, infrastructure monitoring, and urban planning can benefit from this flexible and scalable method, which enhances 3D urban mapping capabilities. Full article