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.

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.

Open-data-driven embeddable quality management services for map-based web applications

Various map-centered web services facilitate citizens’lives.Webmap applications exist for many years already.Due to simplification and improvement of technologies supporting WebGIS,mapbased services become more popular and important nowadays.Data quality assurance for such services is a significant challenge.Since many of such applications intensively use open data,approaches focused on open solutions are required.This work proposes a data-quality concept,which is based on intrinsic and comparable approaches.OpenStreetMap(OSM)allows intrinsic data evaluation.Moreover,it is used as a reference dataset for quality assessment of public-sector-information Open Data layers.Equidistant point(EDP)-based statistics enables to filter out lowquality Open Data features.A data-type model carries out the inventory of OSM data.The comparison of raster web-map tile file sizes and calculation of a simplified data quality indicator make it possible to specify acceptable data quality levels.Embeddable instances of quality assurance web services incorporate data features with acceptable quality.This work provides all required software and data for the deployment of such services under liberal licenses.Concrete instructions allow users to adopt the proposed solutions for their platforms.Some generic use cases illustrate the advantages of the introduced shared web services.