Next Issue
Volume 12, December
Previous Issue
Volume 12, October
 
 

ISPRS Int. J. Geo-Inf., Volume 12, Issue 11 (November 2023) – 28 articles

Cover Story (view full-size image): Can you imagine being able to talk to a map? This paper explores this idea by presenting a novel voice assistant that facilitates user interaction with geospatial web platforms. It explores the nuanced understanding of users’ commands in the geospatial domain, underpinned by a specialised geospatial discourse framework developed through carefully structured prototyping. A survey identified some common English geospatial interaction terminologies. Using ChatGPT, we found that the term suggestions aligned with the survey results, with a notable correlation between the NPL model's probability scores and the prevalence of terms in the survey data. This work marks a meaningful step forward in the field of geospatial data visualisation, proposing a user-friendly, accessible and intuitive interface for complex data interactions. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
14 pages, 12562 KiB  
Article
Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning
ISPRS Int. J. Geo-Inf. 2023, 12(11), 467; https://doi.org/10.3390/ijgi12110467 - 18 Nov 2023
Viewed by 1293
Abstract
Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by [...] Read more.
Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by combining deep learning and low-frequency FCD. Initially, the FCD is cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique. Then, the FCD is split into three categories based on the typical urban road types: one-way one-lane, one-way two-lane, and one-way three-lane, and the deep learning sample data is created using segmentation, rotation, and gridding. Lastly, the number of urban road lanes is obtained by training and predicting the sample data using the LeNet-5 model. The number of urban road lanes was effectively identified from the low-frequency FCD with a detection accuracy of 92.7% through the cleaning and classification of Wuhan FCD. Urban roads can be efficiently covered by the FCD on a regular basis, and lane information can be efficiently collected using deep learning techniques. This method can be used to generate and update lane number information for high-precision navigation maps. Full article
Show Figures

Figure 1

2 pages, 177 KiB  
Editorial
Editorial on Special Issue “Geo-Information Applications in Active Mobility and Health in Cities”
ISPRS Int. J. Geo-Inf. 2023, 12(11), 466; https://doi.org/10.3390/ijgi12110466 - 17 Nov 2023
Viewed by 1032
Abstract
There is growing evidence that active mobility can have a range of positive outcomes for the wider community [...] Full article
(This article belongs to the Special Issue Geo-Information Applications in Active Mobility and Health in Cities)
26 pages, 9045 KiB  
Article
Flash Flood Hazard Assessment along the Red Sea Coast Using Remote Sensing and GIS Techniques
ISPRS Int. J. Geo-Inf. 2023, 12(11), 465; https://doi.org/10.3390/ijgi12110465 - 16 Nov 2023
Viewed by 1476
Abstract
The Egyptian Red Sea coast is periodically exposed to flash floods that cause severe human and economic losses. That is due to its hydro-geomorphological characteristics. Therefore, identifying flash flood hazards in these areas is critically important. This research uses an integrated approach of [...] Read more.
The Egyptian Red Sea coast is periodically exposed to flash floods that cause severe human and economic losses. That is due to its hydro-geomorphological characteristics. Therefore, identifying flash flood hazards in these areas is critically important. This research uses an integrated approach of remote sensing data and GIS techniques to assess flash flood hazards based on morphometric measurements. There are 12 drainage basins in the study area. These basins differ in their morphometric characteristics, and their main streams range between the 4th and 7th order. The morphometric parameter analysis indicates that three wadis are highly prone to flooding, five wadis are classified as moderate hazard, and four wadis are rated under low probability of flooding. The study area has a probability offlooding, which could cause serious environmental hazards. To protect the region from flash flood hazards and the great benefit of rainwater, the study recommended detention, crossing, diversion, and/or storage of the accumulated rainwater by building a number of dams or culverts along the main streams of wadis to minimize the flooding flow. Full article
Show Figures

Figure 1

33 pages, 15148 KiB  
Article
Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach
ISPRS Int. J. Geo-Inf. 2023, 12(11), 464; https://doi.org/10.3390/ijgi12110464 - 14 Nov 2023
Viewed by 1705
Abstract
The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) [...] Read more.
The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), which simulates the stream’s discharge. Considering the HEC-HMS’s estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods. Full article
Show Figures

Figure 1

18 pages, 16789 KiB  
Article
A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China
ISPRS Int. J. Geo-Inf. 2023, 12(11), 463; https://doi.org/10.3390/ijgi12110463 - 13 Nov 2023
Viewed by 1297
Abstract
Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors [...] Read more.
Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change. Full article
Show Figures

Graphical abstract

25 pages, 8987 KiB  
Article
Landscape Sensitivity Assessment of Historic Districts Using a GIS-Based Method: A Case Study of Beishan Street in Hangzhou, China
ISPRS Int. J. Geo-Inf. 2023, 12(11), 462; https://doi.org/10.3390/ijgi12110462 - 12 Nov 2023
Viewed by 1340
Abstract
Historic districts may be damaged during urban renewal. Landscape sensitivity can be used as a method to judge the ability of a landscape to resist change. This study proposes an improved method for assessing landscape sensitivity based on a geographic information system (GIS) [...] Read more.
Historic districts may be damaged during urban renewal. Landscape sensitivity can be used as a method to judge the ability of a landscape to resist change. This study proposes an improved method for assessing landscape sensitivity based on a geographic information system (GIS) according to the characteristics of historic districts. Based on a previous method, this study adds POI big data for comprehensive evaluation and uses objective criteria importance through intercriteria correlation (CRITIC) statistics instead of subjective methods to determine the weights. The assessment framework uses ecological, visual, and cultural sensitivity as primary criteria, which are further defined by several sub-criteria. The Beishan Street Historic District in Hangzhou, China, is used as a case study, and the results of the assessment are shown in the form of sensitivity maps. The results show that the maps can identify buildings in areas of high sensitivity and provide objective indicators for future conservation. Based on the sensitivity maps, this study innovatively used correlation analysis to reveal important interrelationships between ecological, visual, and cultural sensitivity. Assessment factors such as land use type need to be prioritized because they are more closely linked to other factors. Full article
Show Figures

Figure 1

28 pages, 3338 KiB  
Article
Evaluation and Spatiotemporal Differentiation of Cultural Tourism Development Potential: The Case of the Middle and Lower Reaches of the Yellow River
ISPRS Int. J. Geo-Inf. 2023, 12(11), 461; https://doi.org/10.3390/ijgi12110461 - 12 Nov 2023
Viewed by 1297
Abstract
Cultural tourism development potential (CTDP) is the future value and supporting force of the environmental value, economic and social efficiency, innovation ability and supporting system of cultural tourism. At present, there are few relevant studies on CTDP, but the research results on the [...] Read more.
Cultural tourism development potential (CTDP) is the future value and supporting force of the environmental value, economic and social efficiency, innovation ability and supporting system of cultural tourism. At present, there are few relevant studies on CTDP, but the research results on the tourism development potential of cultural heritage are relatively rich, and the existing evaluation methods lack comprehensiveness, dynamics and visualization. Based on systems theory and sustainable development theory, this article attempts to innovate and collect time series data through the entropy method, multi-index comprehensive evaluation method, spatial kernel density estimation method, and centroid transferring model. The temporal and spatial evolution characteristics and the CTDP of 43 cities in the middle and lower reaches of the Yellow River are examined and analyzed. It is found that the CTDP in the middle and lower reaches of the Yellow River is divided into five levels; the overall potential intensity of the research area is small and has significant spatial differences; influenced by the time factor, the interaction and spatial correlation of within the research area are significant; the development of regional cultural tourism has strong regional dependence in the short range. The center of potential gradually moves to the geometric center. This study is significant for promoting the sustainable development of economic tourism in cradles of world civilization. Full article
Show Figures

Figure 1

24 pages, 32634 KiB  
Article
Random Forest Variable Importance Measures for Spatial Dynamics: Case Studies from Urban Demography
ISPRS Int. J. Geo-Inf. 2023, 12(11), 460; https://doi.org/10.3390/ijgi12110460 - 09 Nov 2023
Viewed by 1165
Abstract
Population growth in urban centres and the intensification of segregation phenomena associated with international mobility require improved urban planning and decision-making. More effective planning in turn requires better analysis and geospatial modelling of residential locations, along with a deeper understanding of the factors [...] Read more.
Population growth in urban centres and the intensification of segregation phenomena associated with international mobility require improved urban planning and decision-making. More effective planning in turn requires better analysis and geospatial modelling of residential locations, along with a deeper understanding of the factors that drive the spatial distribution of various migrant groups. This study examines the factors that influence the distribution of migrants at the local level and evaluates their importance using machine learning, specifically the variable importance measures produced by the random forest algorithm. It is conducted on high spatial resolution (100×100 grid cells) register data in Amsterdam and Copenhagen, using demographic, housing and neighbourhood attributes for 2018. The results distinguish the ethnic and demographic composition of a location as an important factor in the residential distribution of migrants in both cities. We also examine whether certain migrant groups pay higher prices in the most attractive areas, using spatial statistics and mapping for 2008 and 2018. We find evidence of segregation in both cities, with Western migrants having higher purchasing power than non-Western migrants in both years. The method sheds light on the determinants of migrant distribution in destination cities and advances our understanding of the application of geospatial artificial intelligence to urban dynamics and population movements. Full article
Show Figures

Figure 1

16 pages, 4209 KiB  
Article
A Fine-Grained Simulation Study on the Incidence Rate of Dysentery in Chongqing, China
ISPRS Int. J. Geo-Inf. 2023, 12(11), 459; https://doi.org/10.3390/ijgi12110459 - 09 Nov 2023
Viewed by 1227
Abstract
Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale [...] Read more.
Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale products are lacking. Thus, an initialized gradient-boosted decision trees (IGBDT) hybrid machine learning model was constructed to fill this gap in grained scale products. Socioeconomic factors, meteorological factors, topographic factors, and air quality factors were used as inputs of the IGBDT to map the statistical dysentery incidence rate data of Chongqing, China, from 2015 to 2021 on the grid scale. Then, dysentery incidence rate grained scale products (1 km) were generated. The products were evaluated using the total incidence of Chongqing and its districts, with resulting R2 values of 0.7369 and 0.5439, indicating the suitable prediction performance of the model. The importance and correlation of factors related to the dysentery incidence rate were investigated. The results showed that socioeconomic factors had the main impact (43.32%) on the dysentery incidence rate, followed by meteorological factors (33.47%). The Nighttime light, normalized difference vegetation index, and maximum temperature showed negative correlations, while the population, minimum and mean temperature, precipitation, and relative humidity showed positive correlations. The impacts of topographic factors and air quality factors were relatively weak. Full article
(This article belongs to the Topic Spatial Epidemiology and GeoInformatics)
Show Figures

Figure 1

15 pages, 5007 KiB  
Article
Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application
ISPRS Int. J. Geo-Inf. 2023, 12(11), 458; https://doi.org/10.3390/ijgi12110458 - 09 Nov 2023
Viewed by 1218
Abstract
Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) [...] Read more.
Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) data of Shenzhen city to implement the funtion2vec model based on the node2vec model and urban tree theory. In this model, we first divide UFE patterns into three categories: Function Replace (FR), Function Newly Added (FNA), and Function Vanishing (FV). Then, we calculate the correlation between those UFE patterns using their functional vectors, resulting in a graph structure representing the urban function evolution network (UFEN). Based on our case study, we obtained the following conclusions: (1) From 2013 to 2022, the UFE in Shenzhen was primarily dominated by FR (89.44%). (2) FV and FNA exhibit a long-tailed distribution, adhering to the 20–80 law. (3) Through the UFEN based on FR, healthcare services are well suited to form mutual complementarities with other functions; science, education, and cultural services demand a higher complementarity with other functions; administrative offices exhibit a strong diversity in their evolutionary patterns; and the integration of transportation hubs with other functions results in a significantly deviating urban function evolution from its original pattern. The above conclusions suggest that function2vec can well express UFE in emerging cities by adding spatial correlation in UFE. Full article
(This article belongs to the Topic Geospatial Knowledge Graph)
Show Figures

Figure 1

23 pages, 8496 KiB  
Article
Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors
ISPRS Int. J. Geo-Inf. 2023, 12(11), 457; https://doi.org/10.3390/ijgi12110457 - 08 Nov 2023
Viewed by 1073
Abstract
Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By [...] Read more.
Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets. Full article
Show Figures

Figure 1

16 pages, 14995 KiB  
Article
Automatic Detection and Mapping of Dolines Using U-Net Model from Orthophoto Images
ISPRS Int. J. Geo-Inf. 2023, 12(11), 456; https://doi.org/10.3390/ijgi12110456 - 07 Nov 2023
Viewed by 1234
Abstract
A doline is a natural closed depression formed as a result of karstification, and it is the most common landform in karst areas. These depressions damage many living areas and various engineering structures, and this type of collapse event has created natural hazards [...] Read more.
A doline is a natural closed depression formed as a result of karstification, and it is the most common landform in karst areas. These depressions damage many living areas and various engineering structures, and this type of collapse event has created natural hazards in terms of human safety, agricultural activities, and the economy. Therefore, it is important to detect dolines and reveal their properties. In this study, a solution that automatically detects dolines is proposed. The proposed model was employed in a region where many dolines are found in the northwestern part of Sivas City, Turkey. A U-Net model with transfer learning techniques was applied for this task. DenseNet121 gave the best results for the segmentation of the dolines via ResNet34, and EfficientNetB3 and DenseNet121 were used with the U-Net model. The Intersection over Union (IoU) and F-score were used as model evaluation metrics. The IoU and F-score of the DenseNet121 model were calculated as 0.78 and 0.87 for the test data, respectively. Dolines were successfully predicted for the selected test area. The results were converted into a georeferenced vector file. The doline inventory maps can be easily and quickly created using this method. The results can be used in geomorphology, susceptibility, and site selection studies. In addition, this method can be used to segment other landforms in earth science studies. Full article
(This article belongs to the Topic Geocomputation and Artificial Intelligence for Mapping)
Show Figures

Figure 1

14 pages, 6596 KiB  
Article
Geo-Visualization of Spatial Occupancy on Smart Campus Using Wi-Fi Connection Log Data
ISPRS Int. J. Geo-Inf. 2023, 12(11), 455; https://doi.org/10.3390/ijgi12110455 - 06 Nov 2023
Viewed by 1301
Abstract
As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy [...] Read more.
As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants’ occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants’ locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research. Full article
Show Figures

Figure 1

15 pages, 5171 KiB  
Article
VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery
ISPRS Int. J. Geo-Inf. 2023, 12(11), 454; https://doi.org/10.3390/ijgi12110454 - 06 Nov 2023
Viewed by 1458
Abstract
Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability [...] Read more.
Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply. Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
Show Figures

Figure 1

16 pages, 3958 KiB  
Article
Bibliometric Insights into the Implications of Urban Built Environment on Travel Behavior
ISPRS Int. J. Geo-Inf. 2023, 12(11), 453; https://doi.org/10.3390/ijgi12110453 - 06 Nov 2023
Viewed by 1457
Abstract
With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades [...] Read more.
With the rapid pace of global urbanization, understanding the impact of the urban built environment on travel behavior has become increasingly significant for developing sustainable and efficient transportation systems. This study conducts a bibliometric review of related research over the past two decades (1997–2023), utilizing 1745 publications from the Web of Science database through network analysis and content analysis. It provides a comprehensive quantitative analysis encompassing publication trends, national and institutional collaborations, and keyword evolution clustering perspectives. The results reveal that (1) academic interest in exploring the implications of the urban built environment on travel behavior has grown markedly, especially in the past decade, with emerging technological approaches and research perspectives; (2) the USA, P.R.CHINA, and the United Kingdom are major research forces in this field, with notable contributions from research institutions in P.R.CHINA and the USA; (3) the “Transportation Research Part” series journals demonstrate extensive influence both in terms of publication count and citation count; (4) through keyword co-occurrence network analysis, three development stages along with five major clusters were identified: travel behavior modeling and public health, active transportation and sustainable development, urban development and carbon emissions, land use and transportation integration, and urban transportation systems and machine learning. Overall, sustained research remains warranted within this field, particularly focusing on selecting new built environment metrics while integrating emerging technologies into travel behavior modeling frameworks. The insights from this study have implications for urban transportation planning and policy, offering guidance on future research directions and policymaking. Full article
Show Figures

Figure 1

17 pages, 4049 KiB  
Article
Decoding Spontaneous Informal Spaces in Old Residential Communities: A Drone and Space Syntax Perspective
ISPRS Int. J. Geo-Inf. 2023, 12(11), 452; https://doi.org/10.3390/ijgi12110452 - 05 Nov 2023
Viewed by 1382
Abstract
Old residential communities are integral parts of urban areas, with their environmental quality affecting residents’ well-being. Spontaneous informal spaces (SIS) often emerge within these communities. These are predominantly crafted by the elderly using discarded materials and negatively impact the environmental quality of communities. [...] Read more.
Old residential communities are integral parts of urban areas, with their environmental quality affecting residents’ well-being. Spontaneous informal spaces (SIS) often emerge within these communities. These are predominantly crafted by the elderly using discarded materials and negatively impact the environmental quality of communities. Understanding SIS emergence patterns is vital for enhancing the environmental quality of old communities; however, methodologies fall short in terms of the quantification of these emergence patterns. This study introduces a groundbreaking approach, merging drone oblique photography technology with space syntax theory, to thoroughly analyze SIS types, functions, and determinants in five Tianjin communities. Utilizing drones and the Depthmap space syntax tool, we captured SIS characteristics and constructed topological models of residences and traffic patterns. We further explored the intrinsic relationships between architectural layout, road traffic, and SIS characteristics via clustering algorithms and multivariate correlation analysis. Our results reveal that architectural layout and road traffic play decisive roles in shaping SIS. Highly accessible regions predominantly feature social-type SIS, while secluded or less trafficked zones lean towards private-type SIS. Highlighting the elderly’s essential needs for greenery, interaction, and basic amenities, our findings offer valuable insights into the revitalization of outdoor spaces in aging communities, into the fostering of urban sustainability and into the nurturing of a balanced relationship between humans and their surroundings. Full article
(This article belongs to the Topic Urban Sensing Technologies)
Show Figures

Figure 1

18 pages, 2445 KiB  
Article
Optimization and Simulation of Mountain City Land Use Based on MOP-PLUS Model: A Case Study of Caijia Cluster, Chongqing
ISPRS Int. J. Geo-Inf. 2023, 12(11), 451; https://doi.org/10.3390/ijgi12110451 - 03 Nov 2023
Viewed by 1012
Abstract
Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation, and urban planning, this study first established three scenarios: economic [...] Read more.
Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation, and urban planning, this study first established three scenarios: economic priority (Econ. Prior.), ecological priority (Ecol. Prior.), and balanced development (BD), and then used the Multi-Objective Planning (MOP) model to calculate the optimal land use structure. Finally, it carried out land use spatial layout optimization based on the Patch-generating Land Use Simulation (PLUS) model in 2035, Caijia Cluster, Chongqing, China. This approach, known as MOP-PLUS modeling, aimed to optimize land use. Meanwhile, the applicability of the PLUS model in simulating land use changes was discussed in small-scale mountainous areas. The results show the following: (1) The “quantity + space” approach in the MOP-PLUS model demonstrated the feasibility of the PLUS model in simulating land use change in small-scale mountainous areas. The overall accuracy (OA) of land use change simulation reached 81.60%, with a Kappa value of 0.73 and a Figure of Merit (FoM) coefficient of 0.263. (2) Land use optimization: Under the Econ. Prior. scenario, economic benefits peaked at 4.06 × 1010 CNY. Urban expansion was the largest, leading to increased patch fragmentation. The Ecol. Prior. scenario yielded the highest ecological benefits, reaching 7.46 × 107 CNY. The urban development pattern exhibited inward contraction, accompanied by urban retrogression. In the BD scenario, economic benefits totaled 3.89 × 1010 CNY, and ecological benefits amounted to 7.16 × 107 CNY. Construction land tended to concentrate spatially, leading to relatively optimal land use efficiency. Therefore, based on a comprehensive consideration of the regional land use constraint policies and spatial layout, we believe that a balance point for land use demands can be found in the BD scenario. It can ensure economic growth without compromising the ecological environment. Full article
Show Figures

Figure 1

18 pages, 3722 KiB  
Article
Enhancing Crop Classification Accuracy through Synthetic SAR-Optical Data Generation Using Deep Learning
ISPRS Int. J. Geo-Inf. 2023, 12(11), 450; https://doi.org/10.3390/ijgi12110450 - 02 Nov 2023
Cited by 1 | Viewed by 1434
Abstract
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is [...] Read more.
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with the traditional data generation methods. These methods have been employed to tackle the imbalanced nature of training data. Nevertheless, they still face limitations in effectively handling minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that the traditional methods struggle to overcome. In this research, we explore the effectiveness of a conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, for addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with a higher quality, which can significantly increase the number of samples for minority classes, leading to a better performance of crop classifiers. For instance, according to the G-mean metric, we observed notable improvements in the performance of the XGBoost classifier of up to 5% for minority classes. Furthermore, the statistical characteristics of the synthetic data were similar to real data, demonstrating the fidelity of the generated samples. Thus, CTGAN can be employed as a solution for addressing the scarcity of training data for minority classes in crop classification using SAR–optical data. Full article
Show Figures

Figure 1

20 pages, 5522 KiB  
Article
Policing Social Disorder and Broken Windows Theory: Spatial Evidence from the “Franeleros” Experience
ISPRS Int. J. Geo-Inf. 2023, 12(11), 449; https://doi.org/10.3390/ijgi12110449 - 31 Oct 2023
Viewed by 1400
Abstract
There is ongoing debate regarding the association between disorder and criminality. The literature has shown mixed, reduced, or no effects between the two phenomena, although few studies have dealt with the problem in terms of social disorder and its spatial heterogeneity. Using official [...] Read more.
There is ongoing debate regarding the association between disorder and criminality. The literature has shown mixed, reduced, or no effects between the two phenomena, although few studies have dealt with the problem in terms of social disorder and its spatial heterogeneity. Using official records, we analyzed crime incidence involving vehicles in Mexico City neighborhoods with a combination of spatial methods, classification algorithms, and non-parametric tests. We found that the presence of people who demand payment for taking care of cars (social disorder) is probably spatially linked to auto parts robbery (crime). It is possible that such social incivility sends a signal that encourages the commission of crimes upon the vehicles, forming spatial clusters due to the undesirable effects of public policies. Our findings enable the broken windows theory to improve its explanatory capacity, considering spatial hypotheses and complementing its explanations with other criminological theories. Full article
Show Figures

Figure 1

19 pages, 16455 KiB  
Article
Spatial and Temporal Evolution of the Characteristics of Spatially Aggregated Elements in an Urban Area: A Case Study of Wuhan, China
ISPRS Int. J. Geo-Inf. 2023, 12(11), 448; https://doi.org/10.3390/ijgi12110448 - 31 Oct 2023
Viewed by 1301
Abstract
Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan [...] Read more.
Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan as a case study and focuses on the spatial agglomeration distribution of urban elements. Point of Interest (POI) data from 2017 to 2021 were collected, and the Block2Vec model was employed to extract the comprehensive geographic information from various elements within the traffic analysis zones (TAZs). Subsequently, identification and division were carried out to access the level of urban spatial element agglomeration. Finally, the spatial–temporal evolution characteristics of urban aggregated elements in the Wuhan metropolitan development area over five years were compared and analyzed. The results indicate the following: (1) urban elements present an obvious circle structure in their spatial agglomeration, with distinct differences observed among different element types; (2) from 2017 to 2021, the Wuhan urban development zone experienced obvious expansion in urban space; (3) increased agglomeration of spatial elements mainly occurred in the surrounding areas of the city, while some areas in the city center displayed weaker element agglomeration and a reduction in various service facilities. The results demonstrate that the method used in this study could effectively identify the spatial agglomeration distribution of urban elements, as well as accurately distinguishing regions with distinct development characteristics. This approach could provide robust support for optimizing land use and urban spatial planning. Full article
Show Figures

Figure 1

23 pages, 13304 KiB  
Article
Identifying Spatiotemporal Patterns of Multiscale Connectivity in the Flow Space of Urban Agglomeration in the Yellow River Basin
ISPRS Int. J. Geo-Inf. 2023, 12(11), 447; https://doi.org/10.3390/ijgi12110447 - 30 Oct 2023
Viewed by 1264
Abstract
The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of [...] Read more.
The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin’s ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin. Full article
Show Figures

Figure 1

38 pages, 31524 KiB  
Article
Comparative Hotspot Analysis of Urban Living Environments and Transit-Oriented Development (TOD) Strategies: A Case Study of Beijing and Xi’an
ISPRS Int. J. Geo-Inf. 2023, 12(11), 446; https://doi.org/10.3390/ijgi12110446 - 30 Oct 2023
Viewed by 1482
Abstract
The quality of urban living environments has become a focal point for local governments and citizens. By conducting a thorough analysis of the human settlement environment, the study can not only gain an intuitive insight into the quality of life of residents but [...] Read more.
The quality of urban living environments has become a focal point for local governments and citizens. By conducting a thorough analysis of the human settlement environment, the study can not only gain an intuitive insight into the quality of life of residents but also propose forward-thinking and sustainable suggestions for areas of improvement. This study optimizes and analyzes open platform data closely related to residents and assesses the suitability of different areas for living from diverse perspectives and methodologies. This study has chosen Beijing and Xi’an as the primary case studies. The local living environment is categorized into residential, living, recreational environment, transportation convenience, and safety. Our evaluation combines subjective and objective analysis methods and considers hotspot and cold spot analyses. This study employs the Analytic Hierarchy Process (AHP) as a subjective analysis method and the entropy method for objective analysis. By integrating both methods, it assesses the living environment conditions of Beijing and Xi’an. Furthermore, using GIS software, hotspot analysis is conducted for both cities, identifying areas of high and low quality. Detailed analysis is subsequently carried out for the low-quality clusters. Ultimately, this study, grounded in the theory of Transit-Oriented Development (TOD), presents recommendations for sustainable development aimed at representative rural towns and streets. City centers in Beijing and Xi’an have high-quality environments, while the outskirts show declining quality. Xi’an has uneven resource distribution, while Beijing is more balanced, with hotspot analyses indicating specific high- and low-quality cluster locations in both cities. These disparities and characteristics of the low-quality clusters offer insights for future urban development. Full article
Show Figures

Figure 1

22 pages, 16004 KiB  
Article
Morphometric Analysis of Trail Network and Tourist Vulnerability in a Highly Frequented Protected Area
ISPRS Int. J. Geo-Inf. 2023, 12(11), 445; https://doi.org/10.3390/ijgi12110445 - 30 Oct 2023
Viewed by 1269
Abstract
Increasing interest in the natural environment and greater hiking activity have resulted in higher anthropogenic pressure in areas characterized by a geographic/physical setting that could present hazardous conditions. The development of these activities is influenced by the peculiar geomorphological and climatic conditions of [...] Read more.
Increasing interest in the natural environment and greater hiking activity have resulted in higher anthropogenic pressure in areas characterized by a geographic/physical setting that could present hazardous conditions. The development of these activities is influenced by the peculiar geomorphological and climatic conditions of the area. Visitors and hikers do not always have adequate cultural background and full awareness of natural dynamics, including the possibility of incurring hazardous conditions. For its cultural and landscape value and extraordinary trail network, the Portofino Promontory is frequented by more than a hundred thousand of hikers a year. However, due to the geomorphological characteristics of the area, the morphological features of the trail network (i.e., exposed paths, steep ups and downs, rocky sections with cables, etc.) and the peculiar meteo-climatic conditions, the number of accidents involving hikers has increased in the most recent years. This research uses a detailed LiDAR survey, a morphometric analysis, and a significant dataset of information on the frequentation of the hiking trail network and on the number of rescue operations carried out by the National Mountain Rescue and Speleological Service (CNSAS). These data have been related to the physical-geographical characteristics of the area. The results can be a useful tool for land management by the Park Authority. Full article
Show Figures

Figure 1

25 pages, 9203 KiB  
Article
Observed Equity and Driving Factors of Automated External Defibrillators: A Case Study Using WeChat Applet Data
ISPRS Int. J. Geo-Inf. 2023, 12(11), 444; https://doi.org/10.3390/ijgi12110444 - 30 Oct 2023
Viewed by 1264
Abstract
Out-of-hospital cardiac arrest (OHCA) causes a high mortality rate each year, which is a threat to human well-being and health. An automated external defibrillator (AED) is an effective device for heart attack-related diseases and is a panacea to save OHCA. Most relevant literature [...] Read more.
Out-of-hospital cardiac arrest (OHCA) causes a high mortality rate each year, which is a threat to human well-being and health. An automated external defibrillator (AED) is an effective device for heart attack-related diseases and is a panacea to save OHCA. Most relevant literature focuses on the spatial distribution, accessibility, and configuration optimization of AED devices, which all belong to the characteristics of the spatial distribution of AED devices. Still, there is a lack of discussion on related potential influencing factors. In addition, analysis of AED facilities involving multiple city comparisons is less considered. In this study, data on AED facilities in two major cities in China were obtained through the WeChat applet. Then, the AED equity at the city and block scales and its socioeconomic factors were analyzed using the Gini coefficient, Lorenz curve, and optimal parameters-based geo-graphical detector (OPGD) model. Results show that the number of AEDs in Shenzhen was about eight-times that of in Guangzhou. The distribution of AEDs in Shenzhen was more equitable with a global Gini of 0.347, higher than that in Guangzhou with a global Gini of 0.504. As for the determinants of AED equity, residential density was the most significant determinant in both Guangzhou and Shenzhen from the perspective of individual effects on AED equity. Differently, due to the aging population in Guangzhou, the proportion of the elderly in blocks was influential to local AED equity. The local economic development level was crucial to local AED equity in Shenzhen. The results of the interaction detector model illustrate that relatively equitable AED distributions were found in the high-density residential areas with a balance of employment and housing, high-aging residential areas, and high-mobility residential areas in Guangzhou. The area with a high level of local economic development, dense population, and large mobility was the area with a relatively equitable distribution of AEDs in Shenzhen. The results of this paper are conducive to understanding the equity of AEDs and its socio-economic determinants, providing scientific reference for the optimization and management of AEDs. Full article
Show Figures

Figure 1

19 pages, 1379 KiB  
Article
Evaluation of the Resilience of the Catering Industry in Hong Kong before and after the COVID-19 Outbreak Based on Point-of-Interest Data
ISPRS Int. J. Geo-Inf. 2023, 12(11), 443; https://doi.org/10.3390/ijgi12110443 - 27 Oct 2023
Viewed by 1350
Abstract
COVID-19 has caused a serious economic shock which challenges the resilience of businesses around the world. Understanding the spatial distribution pattern of business resilience, as well as identifying factors that promote business resilience, is crucial to economic recovery. Most existing studies mainly analyze [...] Read more.
COVID-19 has caused a serious economic shock which challenges the resilience of businesses around the world. Understanding the spatial distribution pattern of business resilience, as well as identifying factors that promote business resilience, is crucial to economic recovery. Most existing studies mainly analyze one side of the concept of resilience, such as how businesses closed, expanded, and innovated, while no studies take all the characteristics of resilience into account and analyze them from a geographical view. To fill this gap, this study first relates the method of calculating stability in ecology to geography, and proposes a point of interest (POI)-based index to evaluate an industry’s resilience in a city. Then, with the catering industry in Hong Kong as an example, the spatial distribution of resilience in June 2020 and December 2020 is investigated using the local indicators of spatial association (LISA) approach. An ordinary least squares (OLS) regression model is adopted to identify impactful factors on resilience. The results reveal that the resilience of restaurants is quite stable in local central areas, but areas near the checking points at Shenzhen in mainland China are severely affected. Most traditional location factors had the benefit of stabilization, while hospitals had negative responses. The presented analysis framework is possible to be easily generalized to other industries or cities. The overall result of the study provides a spatial understanding which would be essential as a reference for future urban planning regarding post-pandemic recovery. Full article
Show Figures

Figure 1

19 pages, 3937 KiB  
Article
What Determinants Will Enhance or Constrain the Spatiality of Agricultural Products with Geographical Indications in Northeast China? An Interpretable Learning Approach
ISPRS Int. J. Geo-Inf. 2023, 12(11), 442; https://doi.org/10.3390/ijgi12110442 - 27 Oct 2023
Viewed by 1139
Abstract
Geographical indication (GI) offers a unique protection scheme to preserve high-quality agricultural products and support rural sustainability at the territorial level. However, not all the areas with traditional agricultural products are acknowledged with a GI. Quantifying the contribution of each factor to geographical [...] Read more.
Geographical indication (GI) offers a unique protection scheme to preserve high-quality agricultural products and support rural sustainability at the territorial level. However, not all the areas with traditional agricultural products are acknowledged with a GI. Quantifying the contribution of each factor to geographical indication agricultural products (GIAPs) can facilitate the formulation of effective policies to improve rural livelihoods. In this study, the random forest (RF) model was applied to investigate the contribution of multi-perspective factors, including nature, society, agriculture and market, on the distribution of GIAPs, and examined the driving causes using interpretable approaches. The empirical findings demonstrate that the RF model is able to accurately capture most of the important factors characterizing GIAPs and to make out-of-sample predictions of the study units which obtain GIs. This study revealed that natural conditions and market demand were contributing aspects to the disparity of GIAPs in Northeast China. The order of determinants was the category of online GIAPs (CatOn) > the number of online GIAPs (NumOn) > the area of black soil (BlaSoil) > the distance to offline stores selling GIAPs (DisOff). Of these, GIAPs was lower than ybase in parts of districts of Jilin and Heilongjiang Provinces when the area of black soil (BlaSoil) gradually increased. When the category and number of online GIAPs (CatOn and NumOn) were less than 20 and 5, respectively, GIAPs were enhanced, especially for 40% of the districts in Liaoning Province. Deepening understanding of GIAPs helps to better target and tailor sustainable development policies. Full article
Show Figures

Figure 1

15 pages, 5019 KiB  
Article
Development of a Voice Virtual Assistant for the Geospatial Data Visualization Application on the Web
ISPRS Int. J. Geo-Inf. 2023, 12(11), 441; https://doi.org/10.3390/ijgi12110441 - 26 Oct 2023
Viewed by 1477
Abstract
Voice assistants can elevate interaction in geospatial data web platforms. This research introduces a voice assistant in the BStreams platform and focuses on understanding user commands in the geospatial domain. We developed a specialised geospatial discourse framework through structured prototyping. A survey with [...] Read more.
Voice assistants can elevate interaction in geospatial data web platforms. This research introduces a voice assistant in the BStreams platform and focuses on understanding user commands in the geospatial domain. We developed a specialised geospatial discourse framework through structured prototyping. A survey with 66 participants revealed prevalent English geospatial terminologies. Using ChatGPT, we found the term suggestions aligned with survey results, with a notable correlation (r = 0.81, p < 0.01) between the NPL model’s probability scores and term prevalence in survey data. Our study also incorporated usability tests on the application, which uses tools like Web Speech API, Leaflet, and Mapbox geocoding. Results from these tests reaffirm the potential of voice assistants in enhancing geospatial data visualisation, though challenges persist in areas like language understanding and domain knowledge. The paper advocates for further research to refine the integration of voice technology in this domain. Full article
Show Figures

Figure 1

20 pages, 10561 KiB  
Article
An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model
ISPRS Int. J. Geo-Inf. 2023, 12(11), 440; https://doi.org/10.3390/ijgi12110440 - 24 Oct 2023
Viewed by 1381
Abstract
Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for [...] Read more.
Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for AR maps heavily relies on manual prefabrication. It leads to repetitive and error-prone prefabrication work, which restricts the design of the dynamic, interactive functions of AR maps. To solve this problem, this paper explored the possibility of automatically generating AR map prefabs using object detection models to establish a data conversion interface from paper maps to AR maps. First, we compared and analyzed various object detection models and selected YOLOv8x to recognize map point symbols. Then, we proposed a method to automatically generate AR map prefabs based on the predicted bounding boxes of the object detection model, which could generate prefabs with corresponding categories and positional information. Finally, we developed an AR map prototype system based on Android mobile devices. We designed an interaction method for information queries in the system to verify the effectiveness of the method proposed in this paper. The validation results indicate that our method can be practically applied to the AR map prefabrication process and can quickly generate AR map prefabs with high information accuracy. It alleviated the repetitive workload established through the manual prefabrication method and had specific feasibility and practicality. Moreover, it could provide solid data support for developing dynamic interactive functions of AR maps. Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop