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Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of autonomous driving and intelligent driver assistance has brought about urgent demands on high-precision road maps. However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer from high costs, object occlusions, and long updating cycles. In the era of ubiquitous mapping, crowdsourced trajectory data offer a new and low-cost data resource for the production and updating of high-precision road maps. Meanwhile, as key nodes in the transportation network, maintaining the currency and integrity of road intersection data is the primary task in enhancing map updates. In this paper, we propose a novel approach for detecting road intersections based on crowdsourced trajectory data by introducing an attention mechanism and modifying the loss function in the YOLOv5 model. The proposed method encompasses two key steps of training data preparation and improved YOLOv5s model construction. Multi-scale training processing is first adopted to prepare a rich and diverse sample dataset, including various kinds and different sizes of road intersections. Particularly to enhance the model's detection performance, we inserted convolutional attention mechanism modules into the original YOLOv5 and integrated other alternative confidence loss functions and localization loss functions. The experimental results demonstrate that the improved YOLOv5 model achieves detection accuracy, precision, and recall rates as high as 97.46%, 99.57%, and 97.87%, respectively, outperforming other object detection models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sketch maps are an abstract and conceptual expression of humans' cognition of geographic space. Humans perceive geographical space at different spatial scales. However, few researchers have considered the spatial relationships of geographic elements in sketch maps at multiple spatial scales. Considering the meso and micro spatial scales, this study analyses the accuracy of the spatial relationships depicted in 52 sketch maps of urban areas, including qualitative orientation, order, qualitative distance, and topological relationships. We utilized OpenStreetMap (OSM) to assess the accuracy of the four spatial relationship representations in the sketch maps. This study evaluates the reliability of spatial relationships in capturing the invariant spatial information of geographic elements in sketch maps. It helps to understand the differences in human cognition of multi-scale space. [ABSTRACT FROM AUTHOR]

Road networks are the skeletal elements of topographic maps at different scales, and road selection is a prerequisite for implementing continuous multiscale spatial representations of road networks. The mesh-based approach is a common, advanced and powerful method for road selection in dense road areas in which small meshes are removed and road segments with the least importance in each mesh are eliminated. However, small meshes in a map can be classified into two types: aggregated small meshes and isolated small meshes. The number of the former is small, and that of the latter is large. Existing methods are generally applicable for the latter, and some or even most spatial characteristics will be lost when they are applied to the former; as a result, the road selection quality will be affected. Therefore, as a supplement to the mesh-based selection method, this paper proposed an automatic generalization method of dense road network areas (areas formed by aggregated small meshes) considering spatial structural features as constraints. First, the aggregated areas of small meshes were identified based on the number of adjoining small meshes, and the boundaries of aggregated areas are extracted and used as hard constraints during mesh elimination. Second, the starting meshes were redefined by simultaneously considering the edge features and mesh density of small meshes, and an ordinal elimination algorithm was proposed to eliminate the meshes in the stroke connection direction. Third, road selection was implemented by identifying the starting meshes and sequentially processing the related mesh pairs. This iterative process continued until all mesh densities of the newly formed meshes are beyond the threshold or the problem becomes a simple elimination problem involving two adjoining small meshes or one isolated small mesh. Finally, a 1:10,000 standard topographic road map for Jiangsu Province, China, was used for validation. The experimental results showed that in the aggregated areas with two small meshes, 31% of the areas obtained the same selection results by using the mesh-based method and the proposed method, and the remaining 69% obtained a more compact result with the proposed method. Moreover, for all aggregated areas with more than two small meshes, the spatial distribution structure of small meshes was preserved better by the proposed method. [ABSTRACT FROM AUTHOR]

Where the streets have no name is probably the preferred place for a volunteer OpenStreetMapper. Launched in 2004, the Open Street Map project aimed to share geographical data based on volunteer mapping and led to the collection of geographical data from almost every country in the world within fifteen years. The increased dissemination of cartographic data via the Internet has been helpful in real life, socially, and has resulted in the number of published documents increasing rapidly. To evaluate the impact of volunteered cartography on scientific research, a science mapping approach was applied to the published literature on the Open Street Map project on the basis of co-occurrence and co-citation analyses, which showed that the main themes (conceptual network) were of technical relevance, collaboration among scholars and among institutes (social network) was not strong, and knowledge and ideas circulated within a limited network. In this study, documents published by OpenStreetMappers were analysed for the first time; thus, it was possible to highlight gaps in volunteered cartography and to discuss further improvements to the Open Street Map project. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the impact of different design methods of bus route maps and dynamic real-time information on the bus route map search efficiency and cognitive load. A total of 32 participants were tested through an experiment of destination bus route searching, and the NASA-TLX scale was used to measure their cognitive load. Two route map schemes were designed according to the research purposes and application status. One was a collinear bus route map with geographic location information based on a realistic map, the other was a highly symmetric straight-line collinear bus route map without map information, and two different types of dynamic real-time information reminder methods were designed (the dynamic flashing of the number of the bus entering the stop, and the dynamic animated flash of the route of the bus entering the stop). Then, four different combinations of the bus route maps were used for testing through the search task of bus routes available for bus destinations. The results indicated no significant difference in the search efficiency between the map-based bus route map and the linear bus route map, but the cognitive load of the map-based bus route map was higher than that of the linear route map. In the presentation of dynamic real-time information, neither the search performance nor the cognitive load of the dynamic flashing of the route of the bus entering the stop was as good as those of the flashing of the number only of the bus entering the stop. In addition, it was found that, compared with men, the cognitive load for women was more affected by geographic information. The optimization strategies of the bus route map information design were proposed by the comprehensive consideration of the feedback of route maps and real-time information. [ABSTRACT FROM AUTHOR]

With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis inWuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google's Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations. [ABSTRACT FROM AUTHOR]

Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users' turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score. [ABSTRACT FROM AUTHOR]

We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into clusters that represent road segments. A new point cluster grouping algorithm, according to the topological relationship and spatial proximity of the point clusters to recover the road network, is then developed. After generating the point clusters, the robust Locally-Weighted Scatterplot Smooth (Lowess) method is used to extract their centerlines. We then propose to build the topological relationship of the centerlines by a Hidden Markov Model (HMM)-based map matching algorithm; and to assess whether the spatial proximity between point clusters by assuming the distances from the points to the centerline comply with a Gaussian distribution. Finally, the point clusters are grouped according to their topological relationship and spatial proximity to form strokes for recovering the road map. Experimental results show that our algorithm is robust to noise and varied sampling rates. The generated road maps show high geometric accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a method to infer road networks from GPS traces. These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections. These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces. We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections. We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment. The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW) algorithm. Our contribution is two-fold. First, we detect potential intersections by clustering the turning points on the GPS traces. Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a "stretch and then compress" strategy based on the DTW algorithm. This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track's time alignment, for example the variance of speed along a road segment. [ABSTRACT FROM AUTHOR]

Web 2.0 enables a two-way interaction between servers and clients. GPS receivers become available to more citizens and are commonly found in vehicles and smart phones, enabling individuals to record and share their trajectory data on the Internet and edit them online. OpenStreetMap (OSM) makes it possible for citizens to contribute to the acquisition of geographic information. This paper studies the use of OSM data to find newly mapped or built roads that do not exist in a reference road map and create its updated version. For this purpose, we propose a progressive buffering method for determining an optimal buffer radius to detect the new roads in the OSM data. In the next step, the detected new roads are merged into the reference road maps geometrically, topologically, and semantically. Experiments with OSM data and reference road maps over 2 an area of 8494 km² in the city of Wuhan, China and five of its 5 km x 5 km areas are conducted to demonstrate the feasibility and effectiveness of the method. It is shown that the OSM data can add 11.96% or a total of 2008.6 km of new roads to the reference road maps with an average precision of 96.49% and an average recall of 97.63%. [ABSTRACT FROM AUTHOR]

Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles. [ABSTRACT FROM AUTHOR]

There has been enormous progress in geospatial data acquisition in the last decade. Centralized data collection, mainly by land surveying offices and local government agencies, has changed dramatically to voluntary data provision by citizens. Among a broad list of initiatives dealing with user generated geospatial information, OpenStreetMap (OSM) is one of the most famous crowd-sourced products. It is believed that the quality of collected information remains a valid concern. Therefore, qualitative assessment of OSM data as the most significant instance of volunteered geospatial information (VGI) is a considerable issue in the geospatial information community. One aspect of VGI quality assessment pertains to its comparison with institutionally referenced geospatial databases. This paper proposes a new quality metric for assessment of VGI accuracy and as well as for quality analysis of OSM dataset by evaluating its consistency with that of a reference map produced by Municipality of Tehran, Iran. A gridded map is employed and heuristic metrics such as Minimum Bounding Geometry area and directional distribution (Standard Deviational Ellipse), evaluated for both VGI and referenced data, are separately compared in each grid. Finally, in order to have a specific output as an integrated quality metric for VGI, its consistency with ground-truth data is evaluated using fuzzy logic. The results of this research verify that the quality of OSM maps in the study area is fairly good, although the spatial distribution of uncertainty in VGI varies throughout the dataset. [ABSTRACT FROM AUTHOR]

The quality aspects of OpenStreetMap (OSM), as the global representation of crowd-sourced mapping, have always been of priomary concern to academics. While the methodologies for checking its quality against the national maps have been implemented by a number of studies, there are minimal works on how to practically improve the quality of OSM towards being an authoritative map source. This paper presents a method for conflating road attributes, namely the name and reference code, of OSM with the Open Data provided by Ordnance Survey (the British national mapping agency). The added values in the proposed methodology include the daily updates and serving of the conflated maps via open Web Services. More importantly, the OSM crowd correction is facilitated by frequently highlighting and web-serving the individual differences. There are currently over 5,800 differences in matching road names and references between the two datasets. In addition to describing the conflation methodology, the different geographic distribution patterns of the identified differences are discussed. A negative effect of the road density on the ratio of the mismatched features between the two datasets is observable, evidenced by their different geographical distribution over the map. It is shown that the best correspondence between attributes exists in the very dense areas, followed by the very low density areas, and lastly in the middle to large sized cities. [ABSTRACT FROM AUTHOR]

Crisis maps play a significant role in emergency responses. Users are challenged to interpret a map rapidly in emergencies, with limited visual information-processing resources and under time pressure. Therefore, cartographic techniques are required to facilitate their map cognition. In this study, we analyzed the exogenous and endogenous disruptions that users needed to overcome when they were reading maps. The analysis results suggested that cartographers' taking the stressors into consideration could promote the cognitive fit between cartographers and users, improving map cognition and spatial information supply–demand matching. This paper also elaborates the course of map visual information processing and related graphic variables to visual attention attributes. To improve the users' map cognition in time-critical emergency situations, crisis map design principles and a methodology were proposed. We developed three fire emergency rescue road maps and performed two evaluations to verify the effectiveness of the principles. Our experiments showed that the principles could effectively facilitate the users' rapid map perception and proper understanding, by reducing their cognitive load, and could improve the quality of the crisis maps to some extent. [ABSTRACT FROM AUTHOR]

Environmental degradation, for example, by wind erosion, is a serious global problem. Despite the enormous research on this topic, complex methods considering all relevant factors remain unpublished. The main intent of our paper is to develop a methodological road map to identify key soil–climatic conditions that make soil vulnerable to wind and demonstrate the road map in a case study using a relevant data source. Potential wind erosion (PWE) results from soil erosivity and climate erosivity. Soil erosivity directly reflects the wind-erodible fraction and indirectly reflects the soil-crust factor, vegetation-cover factor and surface-roughness factor. The climatic erosivity directly reflects the drought in the surface layer, erosive wind occurrence and clay soil-specific winter regime, making these soils vulnerable to wind erosion. The novelty of our method lies in the following: (1) all relevant soil–climatic data of wind erosion are combined; (2) different soil types "sand" and "clay" are evaluated simultaneously with respect to the different mechanisms of wind erosion; and (3) a methodological road map enables its application for various conditions. Based on our method, it is possible to set threshold values that, when exceeded, trigger landscape adjustments, more detailed in situ measurements or indicate the need for specific management. [ABSTRACT FROM AUTHOR]

There is no consensus on the importance of satellite images in the process of memorizing a route from a map image, especially if the route is displayed on the Internet using dynamic (animated) cartographic visualization. In modern dynamic maps built with JavaScript APIs, background layers can be easily altered by map users. The animation attracts people's attention better than static images, but it causes some perceptual problems. This study examined the influence of the number of turns on the effectiveness (correctness) and efficiency of memorizing the animated route on different cartographic backgrounds. The routes of three difficulty levels, based on satellite and road background, were compared. The results show that the satellite background was not a significant factor influencing the efficiency and effectiveness of route memorizing. Recordings of the eye movement confirmed this. The study reveals that there were intergroup differences in participants' visual behavior. Participants who described their spatial abilities as "very good" performed better (in terms of effectiveness and efficiency) in route memorizing tasks. For future research, there is a need to study route variability and its impact on participants' performance. Moreover, future studies should involve differences in route visualization (e.g., without and with ephemeral or permanent trail). [ABSTRACT FROM AUTHOR]

High-quality digital road maps are essential prerequisites of location-based services and smart city applications. The massive and accessible GPS trajectory data generated by mobile GPS devices provide a new means through which to generate maps. However, due to the low sampling rate and multi-level disparity problems, automatically generating road maps is challenging and the generated maps cannot yet meet commercial requirements. In this paper, we present a GPS trajectory data-based road tracking algorithm, including an active contour-based road centerline refinement algorithm as the necessary post-processing. First, the low-frequency trajectory data were transferred into a density estimation map representing the roads through a kernel density estimator, for a seeding algorithm to automatically generate the initial points of the road-tracking algorithm. Then, we present a template-matching-based road-direction extraction algorithm for the road trackers to conduct simple correction, based on local density information. Last, we present an active contour-based road centerline refinement algorithm, considering both the geometric information of roads and density information. The generated road map was quantitatively evaluated using maps offered by the OpenStreetMap. Compared to other methods, our approach could produce a higher quality map with fewer zig-zag roads, and therefore more accurately represents reality. [ABSTRACT FROM AUTHOR]

We present Orinoco, an open-source Python toolkit that applies the fast-marching method to derive a river delta channel network from a water mask and ocean delineation. We are able to estimate flow direction, along-channel distance, channel width, and network-related metrics for deltaic analyses including the steady-state fluxes. To demonstrate the capabilities of the toolkit, we apply our software to the Wax Lake and Atchafalaya River Deltas using water masks derived from Open Street Map (OSM) and Google Maps. We validate our width estimates using the Global River Width from Landsat (GRWL) database over the Mackenzie Delta as well as in situ width measurements from the National Water Information System (NWIS) in the southeastern United States. We also compare the stream flow direction estimates using products from RivGraph, a related Python package with similar functionality. With the exciting opportunities afforded with forthcoming surface water and topography (SWOT) data, we envision Orinoco as a tool to support the characterization of the complex structure of river deltas worldwide and to make such analyses easily accessible within a Python remote sensing workflow. To support that end, all the data, analyses, and figures in this paper can be found within Jupyter notebooks at Orinoco's GitHub repository. [ABSTRACT FROM AUTHOR]

With the rapid development of high-precision road network maps, low-precision road network maps (basic data unrelated to hardware) will need to be directly produced for traditional navigation software from high-precision maps. To do so, large amounts of vector data representing road networks must be simplified and spatial directional similarity in road networks must be maintained while reducing precision. In this study, an elite strategy genetic algorithm based on the grid model is applied to spatial directional adjustment in road networks for producing road network maps for traditional navigation. Firstly, semantic features and critical vertices are extracted from the road network with high precision. Secondly, some high-precision vertices are eliminated under constraints of the digital navigation map. During this process, the local shape maintenance of the road is considered, and the destruction of the spatial topological relationships is avoided. Thirdly, a genetic algorithm for minimizing the total changes in road azimuths at nodes of road networks is developed to maintain spatial directional relationships while reducing precision. Experimental results and visualization effects on the test data of different cities show that this method is suitable for generating road network maps for traditional navigation software from high-precision ones. [ABSTRACT FROM AUTHOR]

With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space–time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user's cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user's cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively. [ABSTRACT FROM AUTHOR]

With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal. [ABSTRACT FROM AUTHOR]

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications. [ABSTRACT FROM AUTHOR]

Law enforcement agencies (LEA) have long attempted to determine crime patterns, determinants of crime, and ways to improve security. For several years, LEAs have utilized geographic information systems (GISs) to better understand the spatial nature of crimes and possible spatiotemporal patterns. While previous analyses have yielded spatial clusters of high crime areas (i.e., hot spots), these analyses have lacked specificity at the street level. This research builds on the concept of a hot spot area and creates linear statistically significant spatial crime clusters at the street level, namely hot streets. The identification of hot streets has the potential to assist LEAs in reducing crime through increased patrolling and refocusing efforts at the street level. This study created a GIS-based geoprocessing model to analyze different crime types at varying temporal scales and in different cities (Atlanta, Georgia and Houston, Texas), classifying streets based on four crime cluster confidence levels. The research found that the geoprocessing model successfully streamlined the creation of crime hot streets in the two study areas, led to the development of multi-scale hot street maps, and can be used and edited by anyone who wants to perform linear cluster analysis in other topics. [ABSTRACT FROM AUTHOR]

The main purpose of this article was to develop a method of researching accessibility in the event of a flood through the application of measurement based on mobility. In the course of the research, it has been proven that changes in mobility (and the related travel speed) are too significant to be ignored when studying accessibility in unusual circumstances. The vast majority of existing accessibility studies rely primarily on speed models, which – in the event of a flood – do not indicate the external effects of the natural disaster. On the basis of the conducted research it has been stated that the occurrence of a flood has a significant impact on changes in the spatial distribution of traffic and its related speeds. Such changes vary depending on the particular means of transport. With the most commonly applied methods of measuring accessibility, which are customarily based on speed models, the changes we observed would not be recorded. The application of mobility-based research in the analyses of accessibility – especially in the event of a flood – indicates the disaster's influence on the capacity of the road network, and thus, it allows for more effective flood-risk management. Furthermore, this article also demonstrates the possibility of applying source materials available in most member states of the EU, i.e., flood-risk maps and digital terrain models (NMPT), for the purposes of analysing and identifying road section closures within the transport network after the occurrence of a flood. [ABSTRACT FROM AUTHOR]

For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design of Google Maps and other map services is limited to the provision of map support at 20 levels of high-resolution precision. An ideal map should include information on roads, autonomous vehicles, and Internet of Things (IOT) facilities that support autonomous driving. The aim of this study was to design a map suitable for the control of autonomous vehicles in Gyeonggi Province in Korea. This work was part of the project "Building a Testbed for Pilot Operations of Autonomous Vehicles". The map design scheme was redesigned for an autonomous vehicle control system based on the "Easy Map" developed by the National Geography Center, which provides free design schema. In addition, a vector-based precision map, including roads, sidewalks, and road markings, was produced to provide content suitable for 20 levels. A hybrid map that combines the vector layer of the road and an unmanned aerial vehicle (UAV) orthographic map was designed to facilitate vehicle identification. A control system that can display vehicle and sensor information based on the designed map was developed, and an environment to monitor the operation of autonomous vehicles was established. Finally, the high-precision map was verified through an accuracy test and driving data from autonomous vehicles. [ABSTRACT FROM AUTHOR]

Urban roads are the lifeline of urban transportation and satisfy the commuting and travel needs of citizens. Following the acceleration of urbanization and the frequent extreme weather in recent years, urban waterlogging is occurring more than usual in summer and has negative effects on the urban traffic networks. Extracting flooded roads is a critical procedure for improving the resistance ability of roads after urban waterlogging occurs. This paper proposes a flooded road extraction method to extract the flooding degree and the time at which roads become flooded in large urban areas by using global positioning system (GPS) trajectory points with driving status information and the high position accuracy of vector road data with semantic information. This method uses partition statistics to create density grids (grid layer) and uses map matching to construct a time-series of GPS trajectory point density for each road (vector layer). Finally, the fusion of grids and vector layers obtains a more accurate result. The experiment uses a dataset of GPS trajectory points and vector road data in the Wuchang district, which proves that the extraction result has a high similarity with respect to the flooded roads reported in the news. Additionally, extracted flooded roads that were not reported in the news were also found. Compared with the traditional methods for extracting flooded roads and areas, such as rainfall simulation and SAR image-based classification in urban areas, the proposed method discovers hidden flooding information from geospatial big data, uploaded at no cost by urban taxis and remaining stable for a long period of time. [ABSTRACT FROM AUTHOR]

Extracting the split line of narrow and long patches is important for the generalization of land-use thematic data. There are two commonly used methods for extracting the split lines: One is based on Delaunay triangulation and the other is based on straight skeletons. However, it is difficult for the straight skeleton method to preserve geometric structure and topological consistency with the original data when dealing with polygons that have irregularity and complexity of junctions. Therefore, we propose an improved jitter elimination and topology correction method for split lines based on a constrained Delaunay triangulation. First, a split line adjustment algorithm based on the geometric structure of the polygon is proposed to eliminate the jitters. Second, a split line topology correction algorithm is proposed for nodes with degree 1 or degree 2, considering the boundary topological constraint. The reliability of the proposed method is verified by comparing it with the straight skeleton method using sample data and the superiority of the proposed method is verified by using actual data from China's geographical conditions census in the Guizhou province. [ABSTRACT FROM AUTHOR]

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions. [ABSTRACT FROM AUTHOR]