Towards Exploring Patterns of Editing Behavior on OpenStreetMap

OpenStreetMap has a large number of volunteers.There is a hypothesis that volunteers with different cultural backgrounds may have different editing behaviors when contributing to OSM.It may be strongly related to data quality and data reliability on OSM.As for the heterogeneity and the reliability of OSM data,previous research usually focuses on the geometric accuracy,spatial location accuracy and semantic integrity of OSM data,while few researchers have analyzed these problems from the perspective of editing behavior.On the grounds of relationship between mapping motivation and editing behavior,the dispersion of editing trajectory and clockwise direction index are proposed in the paper to explore whether the volunteers are sufficiently motivated and knowledgeable.In the experiments,the historical OSM data of four countries suggested that developed countries have lower trajectory dispersion.The lower degree of trajectory dispersion reflects the higher concentration and professionalism of volunteers.A high degree of drawing direction consistency shows volunteers who mapped French data were natives with local knowledge.From this point of view,this paper verifies that volunteer editing behavior is an effective method to analyze data quality heterogeneity and data reliability.

Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints

Currently,very few roof shape information for complex buildings is available on OSM.Moreover,additional data requirements(e.g.3D point clouds)limit the applicability of many roof reconstruction approaches.To mitigate this issue,we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints:the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints.First,it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm.Second,a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions.Then,a set of selection rules are defined to rank partitions,and the best ones are chosen for roof shape recommendation.Finally,a set of combination rules and a symmetry rule are defined.It enables to evaluate the probability of a footprint being a certain combination of roof shapes.Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17–45%and from a prior probability of 0.29–14.3%,removing 60%and 93%of the incorrect roof shape options,respectively.

Layout graph model for semantic façade reconstruction using laser point clouds

Building façades can feature different patterns depending on the architectural style,function-ality,and size of the buildings;therefore,reconstructing these façades can be complicated.In particular,when semantic façades are reconstructed from point cloud data,uneven point density and noise make it difficult to accurately determine the façade structure.When inves-tigating façade layouts,Gestalt principles can be applied to cluster visually similar floors and façade elements,allowing for a more intuitive interpretation of façade structures.We propose a novel model for describing façade structures,namely the layout graph model,which involves a compound graph with two structure levels.In the proposed model,similar façade elements such as windows are first grouped into clusters.A down-layout graph is then formed using this cluster as a node and by combining intra-and inter-cluster spacings as the edges.Second,a top-layout graph is formed by clustering similar floors.By extracting relevant parameters from this model,we transform semantic façade reconstruction to an optimization strategy using simulated annealing coupled with Gibbs sampling.Multiple façade point cloud data with different features were selected from three datasets to verify the effectiveness of this method.The experimental results show that the proposed method achieves an average accuracy of 86.35%.Owing to its flexibility,the proposed layout graph model can deal with different types of façades and qualities of point cloud data,enabling a more robust and accurate reconstruc-tion of façade models.

Urban spatial structure analysis:quantitative identification of urban social functions using building footprints

Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems.However,many of the existing methods are limited because of data quality and availability,and usually yield inaccurate results due to the unclear description of urban social functions.In this paper,we present an investigation on urban social function based spatial structure analysis using building footprint data.An improved turning function(TF)algorithm and a selforganizing clustering method are presented to generate the variable area units(VAUs)of high-homogeneity from building footprints as the basic research units.Based on the generated VAUs,five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block.Within these spatial metrics,three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block,quantitatively.Consequently,the urban structures can be clearly observed by an analysis of the spatial distribution patterns,the development trends,and the hierarchy of different social functions.The results of a case study conducted for Munich validate the effectiveness of the proposed method.

An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network

The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices.In this paper,we aim to propose a novel and web-based interactive platform,VGI3D,to overcome these challenges.The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic informa-tion(VGI)platform,even though not all these images are of high quality.Our interactive platform can effectively obtain each 3D building model from images in 30 seconds,with the help of user interaction module and convolutional neural network(CNN).The user interaction module provides the boundary of building facades for 3D building modeling.And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images.Moreover,user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users.Meanwhile,we conducted a usability testing and collected feedback from participants to better optimize platform and user experience.In general,the usage of VGI data reduces labor and device costs,and CNN simplifies the process of elements extraction in 3D building modeling.Hence,our proposed platform offers a promising solution to the 3D modeling community.

Data-driven approach to learning salience models of indoor landmarks by using genetic programming

In landmark-based way-finding,determining the most salient landmark from several candidates at decision points is challenging.To overcome this problem,current approaches usually rely on a linear model to measure the salience of landmarks.However,linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience.Furthermore,the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited.With the aim of overcoming these gaps,we propose learning a non-linear salience model by means of genetic programming.We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls.Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs.The results from this experiment showed that in 76%of the cases,the most salient landmark(according to the volunteers’perception)was correctly predicted by our proposed approach.This accuracy rate is considerably higher than the ones achieved by conventional linear models.

Analytics of big geosocial media and crowdsourced data

Numerous crowdsourcing and social media platforms such as CrowdSpring,Idea Bounty,DesignCrowd,Facebook,Twitter,Flickr,Weibo,WeChat,and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful infor-mation for detecting traffic patterns,mitigating security risks and other types of time-critical events,discovering social structures characteristics,predicting human movement,etc.Crowdsourcing,also known as volunteered geographic information(VGI),has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies(Chen et al.,2016;Niu&Silva,2020).However,analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general,the inherent noisy data that requires high-performance cost of preprocessing,and the heterogeneity in quality and quantity of sources.In particular,geosocial media data and their derived metrics can provide valuable insights and policy strategies,but they require a deep understanding of what the metrics actually measure(Zook,2017).All of these underpin complex assessments,not mention-ing the ethnic and privacy issues.Therefore,new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms.

Detecting repetitive structures on building footprints for the purposes of 3D modeling and reconstruction

Repetitive structures of a building share features in terms of geometries and appearance and,therefore,the 3D information for these structures can be transferred from one specification to another for the purpose of 3D modeling and reconstruction once they are identified as repetitive structures.In this paper,a novel approach is proposed for the detection of the repetitive structures specified by the polygons of a building’s footprints.Instead of directly operating on the polygon in 2D space,the polygon is converted into a bend angle function representation in 1D space,whereby an extrusion is represented as a closed polygon intersected by the x-axis and located above it,while an intrusion is represented as a closed polygon below the x-axis.In this way,a polygon of a footprint is decomposed into a number of extrusions and intrusions which can in turn be processed.The task of detecting any repetitive structures specified in a building’s footprints then becomes the task of clustering the intersected polygons in the bend angle function space.The extrusions/intrusions which can be placed in the same clusters can be regarded as repetitive structures.Experiments show that this proposed approach can detect repetitive structures with different sizes,orientations and complexities.

Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data

Public medical facilities that are closely related to the health of residents have been recognised as one of the most crucial elements in sustainable urban planning.For the sake of social equality of medical services(especially for emergency medical conditions),the spatial distributions of medical resources need to be accurately measured and continuously optimized.This study presents an effective method to examine night emergency hospital visit and analyse its spatiotemporal characteristics using float car data(FCD).By extracting the hospital service areas,the two-step floating catchment area(2SFCA)methodology was improved to calculate hospital accessibility.Then,the balance between hospital accessibility and population density was analysed.In addition,we investigated the relationship between individual hospital choice preferences and hospital level and analysed several factors that affect individual choices.These results help us understand the special requirements and need of emergency hospital travel in cities and identify areas where medical resources are scarce.They can be used as guidance for urban hospital planning and construction.And the approach of hospital access behaviour investigation and the improved 2SFCA method can also provide insights for other activity-based travel behaviour research.