OSM Data Research Articles

Mathematical Graph Based Urban Simulations as a Tool for Biomimicry Urbanism?

Biomimicry studies natural systems and attempts to use the gained knowledge and understanding to solve human problems. Can biomimicry, if applied in urban planning, help to make our cities more sustainable or, precisely, more friendly for walkable and 15-minute city models? Various researchers identify the following features of natural systems as form fits function, catalysis of cooperation, local contextuality, continuity of development, diversity, integrity, redundancy, decentralization, multifunctionality, and less energy consumption (e.g. TOD if the energy needed for transportation is considered), hierarchy, fractality, etc. The presented research has two objectives: analyze the possibility of describing and evaluating the degree of expression of the features of natural systems in urban spatial structures to use them for comparison, benchmarking, or creation of a biomimicry index; evaluate if cities with more strongly expressed biomimicry features could be considered more walkable and close to the 15-minute city concept. To give answers to the above-presented questions Riga, Vilnius, and Tallinn were investigated. They represent both certain similarities and differences in terms of history, urban planning, political context, size, etc. A simulative mathematical graph model was used as the main tool to describe and analyze complex urban structures based on spatial configurations. Various graph centralities were employed to describe and compare the expression of natural systems’ features in the selected cities. OSM data was used to overlap graph models with such information as the density of inhabitants, concentrations of various points of interest in order to evaluate walkability and 15-minute city friendliness. The research results could be seen as an important step for better understanding of informal principles of biomimicry that can help transform urban areas.

Identifying Land Use Functions in Five New First-Tier Cities Based on Multi-Source Big Data

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

A CLASSIFICATION MODEL FOR THE INFERENCE OF SPATIAL PRECISION OF OPENSTREETMAP BUILDINGS WITH INTRINSIC INDICATORS

Abstract. To evaluate the quality of OSM data, similarities between OSM features and their homologous features represented in a reference database are relevant metrics. However, reference databases do not exist everywhere or are not freely available. Thus, having data quality assessment methods that rely only on intrinsic indicators (i.e. based on data itself without considering external information) would be useful in these cases. This article specifically uses the radial distance as a target quality metric to measure the quality of shapes. Its aim is to build a random-forest based classification method that reconstructs whether this distance is higher or lower than a specified threshold, using only intrinsic indicators as inputs. The classification algorithm is evaluated on a first dataset by computing the ROC (Receiver Operating Characteristic) curve and using the AUC (Area Under Curve) as an evaluation metric. The transferability of the resulting algorithm is then evaluated by measuring its performance on a second, distinct dataset. The experiments show that the algorithm performs reasonably well on both the initial and the second dataset, and that intrinsic indicators give relevant information to infer comparison-based shape quality (i.e. the radial distance).

Analysis of Flood Vulnerability Levels Using Overlay Method With System-Based Scoring Geographical Information (Case Study: District Tangerang)

This research aims to determine the factors of flood disasters in Tangerang district, which consist of land height, slope, soil type, land use, river density, and rainfall, and to find out the results of flood mapping in Tangerang district. The research method used is the overlay method with scoring. This research uses secondary data consisting of rainfall data for the period 2022-2020, shp Admin data from Tangerang district, watershed data throughout Indonesia, OSM data throughout Indonesia, slope data throughout Indonesia, DEM data throughout Indonesia, DSMW data throughout Indonesia, 2017 landcover data. Data were processed using Microsoft Excel software, Arc GIS 10.8, and QGIS 3.26.0. Based on the results obtained, the flood disaster factors in Tangerang district that are very dominant in influencing the occurrence of floods in Tangerang district are soil type and slope. Meanwhile, the results of flood mapping in Tangerang district are categorized into three classes: not vulnerable, moderately vulnerable, and fragile. Four sub-districts in Tangerang Regency are not prone to flood disasters, including Panongan, Legok, Cisauk, and Pagedangan sub-districts. Meanwhile, the Tangerang district, which is categorized as quite vulnerable, consists of 9 sub-districts, namely the sub-districts of Kelapa Dua, Curug, Cikupa, Tigaraksa, Jambe, Solear, Jayanti, Cisoka, Balaraja. Meanwhile, there are 16 sub-districts in Tangerang Regency which are categorized as very vulnerable, namely Pasar Kemis, Sindang Jaya, Kresek, Gunung Kaler, Sukamulya, Sepatan, Sepatan Timur, Rajeg, Kemiri, Kronjo, Mekar Baru, Mauk, Sukadiri, Pakuhaji, Teluk Naga, Kosambi districts.

Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data

This study aims at a comparative analysis of quantitative data, namely, OSM and BDOT10k. Analyses were conducted in a 1 km2 hexagonal grid, in seven test counties located in different regions of Poland, differing in the degree of urbanization, land cover and natural environment. It is assumed that the authors’ consolidated regional classification of the Compound Correspondence Index CCIRn is attributed to the geometric mapping unit based on TOPSIS values, and their statistical measure of dispersion enables the comparison of datasets for individual geographically disjointed areas according to uniform criteria, e.g., the number of topographic features stored in analyzed datasets, both polygonal (buildings, forests, surface water) and linear (roads, watercourses, railroads). The final results of the regional assessment outperform the local classification giving a higher level of data compliance. Overestimation of regional concordance ranges from 9 to 20% of the county area, with an average of 3% reduction in the area where the two datasets (BDOT10k and OSM) have comparable information ranges. Areas of medium and high nonconformity are decreased by an average of 2.4%.

Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.

Analysis of cycling network evolution in OpenStreetMap through a data quality prism

Abstract. Cycling practice has been constantly increasing for several years and the COVID crisis has just accelerated the process. Indeed, more and more municipalities have developed new cycle paths to facilitate cycling. Considering this increasing interest for cycling, it makes sense to study how this recent evolution is reflected in the underlying representation of the cycling network in the geographic databases. Main studies analysing the evolution of the road network focus on the motor vehicle network in the major cities of the world. These studies do not seem applicable to cycling network specially to some low population density areas or even to smaller cities. This paper analyses the changes in the cycling network through OSM data from a data freshness perspective. These changes can be either updates from changes in the real-world network or upgrades to the network. To these end, we propose a method using a Monte Carlo simulation (MCS) to analyse the frequency of changes in cycling routes in several areas with different population density, all in the Loire Valley region in France. We also define the cycling network, which is a very complex concept and we explain how it is represented in OSM data and suffers from different data quality issues. Results show that the number of changes across time are similar in areas having a similar population density, while being lower in low population density areas. These phenomena is higher in the cycling network compared to other networks.

Semi-automated Pipeline to Produce Customizable Tactile Maps of Street Intersections for People with Visual Impairments

Abstract. Street intersections are very challenging for people with visual impairments. Manually produced tactile maps are an important support in teaching and assisting independent journeys as they can be customized to serve the visually impaired audience with diverse tactile reading and mobility skills in different use scenarios. But the manual map production involves a huge workload that makes the maps less accessible. This paper explores the possibility of semi-automatically producing customizable tactile maps for street intersections. It presents a parameterized semi-automated pipeline based on OSM data that allows the maps to be customized in size, map features, geometry processing choices, and symbolizations. It produces street intersection maps in two scales of three sizes, with different levels of details and styles.

Statistical modelling of digital elevation models for GNSS-based navigation

ABSTRACT Recently, smart mobility has become a important activity in transportation systems such as public, autonomous and shared transports. These systems require reliable navigation applications that lead to precise localisation and optimised route. The GPS system may face problems such as signal degradation caused by conical effects, affecting the reliability and accuracy of the signal, or signal loss in poor visibility environments. By using other sensors, the vehicle location system can overcome these GPS problems. This work focuses on the estimation of the inclination, which will be used to optimise the route planning for the EV or HEV especially in order to control the energy consumption. This paper presents a multi-sensor fusion method, based on GNSS, INS, OSM and DEM data fused using a non-linear particle filter, to estimate and improve the slopes of road segments. A new statistical modelling of the DEM errors related to the spatial sampling of elevation data is proposed. This method is based on the definition of a geometrical window, called Adjacent Sliding Window (ASW), which dynamically selects the elevation data in the vicinity of the road. The proposed method is evaluated in a suburban transport network. The experimental results show the benefits of the vehicle attitude and road slope estimation accuracies.

Space Based Assessment of Base Transceiver Stations in Kwamba, Niger State, Nigeria

Global System for Mobile Communications (GSM) started operations in Nigeria on the 16th of May, 2001. An important feature of the communication system is the Base Transceiver Stations (BTS). However, their installations have brought about public concerns and fears, specifically to issues relating to environmental risk, public health and sometimes construction problems that might result to mast collapse. This study is aimed at identifying the spatial distribution of BTS in Kwamba, analysing the terrain of the study area and examining their level of compliance to NESREA standards for siting a BTS. The data for the Kwamba study was collected from two sources; Primary and Secondary sources. The primary data include the use of GPS to obtain coordinates of the GSM base stations and their respective elevations. The Secondary and geographic data includes Google Earth imagery obtained from Google Earth, Landsat images, SRTM data from USGS Earth Explorer and shape files (roads etc) from OSM data. ArcMap was used to plot a map showing the distribution and slope extraction of the study area to provide information on the terrain. Also, using NESREA stated standard, 10metre buffer test on all existing BTS in Kwamba was carried out using ArcMap. Findings from the research revealed that there are 24 BTS distributed randomly in Kwamba. 7 out of the 24 failed the 10metre buffer test, meaning they were not in compliance with 10m setback distance as stated by NESREA for locating a BTS close to residential homes. The study recommends that stakeholder(s) imbibe the use of GIS techniques in the siting of BTS as it will assist in monitoring of non-compliance to the regulated standard of NESREA by the service providers. The adaptation of use of GIS techniques will also assist the regulatory bodies in effectively carrying out constant supervision of the service providers.

Simulation of urban functional zone air temperature based on urban weather generator (UWG): a case study of Beijing, China

ABSTRACT Rapid urbanization has led to many urban thermal environment problems. Most studies focus on analysing the urban thermal environment from the perspective of land-use type, and often at a large scale. Thus, research of urban spatial thermal environment at a block scale with different urban functional zone (UFZ) becomes the focus of this paper. UFZ refers to urban planning units with similar socio-economic functions, which often have similar energy consumption and outdoor thermal environment. In this paper, the urban weather generator (UWG) model is used to quantitatively analyse the spatial thermal environment of Beijing. To accurately simulate the urban thermal environment, the underlying surface used in the simulation is replaced by the finely classified underlying surface of the urban functional area. In this paper, we first use POI + OSM data to divide the functional areas of Beijing. The recognition accuracy of functional area is 85.6%. Then, the UWG model is used to simulate the temperature and humidity differences in different functional areas, and finally the corresponding urban and rural weather stations were used to validate its accuracy. The results show that the model calibration method can effectively improve the simulation accuracy of UWG thermal environment, the R2 is increased by about 0.12, and the RMSE is reduced by about 0.84. The canopy temperature of different urban functional areas from high to low is: industrial area>commercial area>residential area>public area>road and traffic area>green space and square area. This model is more suitable for areas with relatively homogenous urban morphology and sparse urban vegetation. The thermal environment simulation results of the same urban functional area are basically the same, with slight differences in the temperature peaks, and the average temperature in the city centre is higher than that in the suburbs.

Automatic 3D Building Reconstruction from OpenStreetMap and LiDAR Using Convolutional Neural Networks

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

Use of infrastructure parameters from OSM data to assess cyclist safety in European cities

Use of infrastructure parameters from OSM data to assess cyclist safety in European cities

ASSESSING GEO-TYPICAL TECHNIQUES FOR MODELING BUILDINGS USING THERMAL SIMULATIONS

Abstract. Building modeling from remote sensing data is essential for creating accurate 3D and 4D digital twins, especially for temperature modeling. In order to represent buildings as gap-free, visually appealing, and rich in details models, geo-typical prototypes should be represented in the scene. The sensor data and freely available OSM data are supposed to provide guidelines for best-possible matching. In this paper, the default similarity function based on intersection over union is extended by terms reflecting the similarity of elevation values, orientation towards the road, and trees in the vicinity. The goodness of fit has been evaluated by architecture experts as well as thermal simulations with a thermal image as ground truth and error measures based on mean average error, root mean square and mutual information. It could be concluded that while intersection over union measure still seems to be most preferred by architects, slightly better thermal simulation results are yielded by taking into account all similarity functions.

Spatial Pattern of the Walkability Index, Walk Score and Walk Score Modification for Elderly

Contemporary cities require excellent walking conditions to support human physical activity, increase humans’ well-being, reduce traffic, and create a healthy urban environment. Various indicators and metrics exist to evaluate walking conditions. To evaluate the spatial pattern of objective-based indicators, two popular indices were selected—the Walkability Index (WAI), representing environmental-based indicators, and Walk Score (WS), which applies an accessibility-based approach. Both indicators were evaluated using adequate spatial units (circle buffers with radii from 400 m to 2414 m) in two Czech cities. A new software tool was developed for the calculation of WS using OSM data and freely available network services. The new variant of WS was specifically designed for the elderly. Differing gait speeds, and variable settings of targets and their weights enabled the adaptation of WS to local conditions and personal needs. WAI and WS demonstrated different spatial pattern where WAI is better used for smaller radii (up to approx. 800 m) and WS for larger radii (starting from 800 m). The assessment of WS for both cities indicates that approx. 40% of inhabitants live in unsatisfactory walking conditions. A sensitivity analysis discovered the major influences of gait speed and the β coefficient on the walkability assessment.

A Graph-Cut-Based Method for Road Labels Making With OSM Data

A Graph-Cut-Based Method for Road Labels Making With OSM Data

Handling OpenStreetMap georeferenced data for route planning

This work proposes an architecture to treat georeferenced data from the OpenStreetMap to plan routes. The methodology considers the following steps: collecting data, incorporating data into a data manager, importing data into a data model, executing routing algorithms, and visualizing routes. Our proposal incorporates the following features characterizing each street segment: safety & security, comfort, accessibility, air quality, time, and distance. Routes can be calculated considering any specified weighting system of these features. The outcome of the application of this architecture allows to calculate and visualize routes from georeferenced data, which can support researchers in the study of multi-criteria routes. Furthermore, this framework enhances the OSM data model adding a multi-criteria dimension for route planning.

MAP GENERATION FOR RETRO GAMING ENGINES

Abstract. Current attempts to integrate gaming, geospatial and AEC technologies are driven by increased hardware capabilities and target virtual worlds as close to reality as possible. In this paper we pursue a converse destination and populate low-tech virtual worlds from geospatial data, building and city models, specifically for retro gaming engines in spatial chat tools. We consider an island, a building and a city district scenario and populate these scenarios from OSM, IFC, and CityGML data sources. The derived worlds are targeting use cases in business, educational and recreational settings. Based on a prototypical implementation, we study the feasibility and limitations of good quality retro gaming map generation with automatic means from existing data sets, but also the potential of such worlds for Green IT, accessibility and inspiration of new user groups. We describe the algorithms and processes to generate the maps and outline the concept for a user survey. Beyond that, by discussing map generation techniques from classical gaming in context of and comparison with geospatial and AEC practices, this paper contributes a retrospective of early gaming techniques that might be both entertaining and informative for current development practice.

A comparison of two deep-learning-based urban perception models: which one is better?

Urban perception is a hot topic in current urban study and plays a positive role in urban planning and design. At present, there are two methods to calculate urban perception. 1) Using a model to learn image features directly automatically; 2) Coupling machine learning and feature extraction based on expert knowledge (e.g. object proportion) method. With two typical streets in Wuhan as the study area, video data were recorded and used as the model input. In this study, two representative methods are selected: 1) End to end convolution neural network (CNN-based model); 2) Based on full convolution neural network and random forest (FCN + RF-based model). By comparing the accuracy of two models, we analyze the adaptability of the model in different urban scenes. We also analyze the relationship between CNN-based model and urban function based on POI data and OSM data, and verify its interpretability. The results show that the CNN-based model is more accurate than FCN + RF-based model. Because the CNN-based model considers the topological characteristics of the ground objects, its perception results have a stronger nonlinear correlation with urban functions. In addition, we also find that the CNN-based model is more suitable for scenes with weak spatial heterogeneity (such as small and medium-sized urban environments), while the FCN + RF-based model is applicable to scenes with strong spatial heterogeneity (such as the downtown areas of China’s megacities). The results of this study can be used as a reference to provide decision support for urban perception model selection in urban planning.

DETERMINATION OF TRANSPORT ACCESSIBILITY IN THE FORMATION OF TOURIST ROUTES USING QGIS AND GRASS GIS

The creation of modern tourist products contributes to the attractiveness of our country in the market of tourist services. Transport accessibility is an important aspect for creating tourism products. This study considers the algorithm for determining transport accessibility for the projection of tourist travel routes, based on the use of geographic information technologies. Transport accessibility is determined by collecting and filtering data and subsequent creation of isochron maps, which show the time distance when moving a particular mode of transport along the lines of the road network from points of interest. Attractions of Ternopil region were chosen as such points of interest for tourists. The paper uses open OSM data on tourist facilities and the road network. Data preparation was performed using PostgreSQL software. When performing the study, the QGIS program was used, in which data processing was performed. Spatial analysis is performed in the spatial information processing environment GRASS GIS; data processing modules or GRASS GIS plug-ins are used. The proposed algorithm allows forming a database of tourist facilities, transport routes, to define more accurately existing tourist routes, to create new ones. As a test in the course of the study, the route of the tour of Ternopil was designed, in the construction of which an isochron map was used. Spatial analysis helps to better determine the path of the tourist tour and its duration.