A Template Matching Method for Enhancing and Generalizing OpenStreetMap Building Data

With the favorable characteristics of wide coverage, free sharing, quasi real-time and so on, Volunteer Geographic Information (VGI) is rapidly becoming a significant supplement of the traditional and professional geographic information, such as the OpenStreetMap (OSM) (Goodchild, 2007; Elwood, 2008). However, this type of data also has some obvious problems because it is gathered by individuals with no professional training, for example, the quality of data is not standardized and the scale is not unified. Therefore, further processes are essential to perform analysis and mapping on VGI, such as the squaring and generalization operations on the OSM building layer (Lokhat & Touya, 2016). By considering the typical template characteristics of building distributions and representations, this study proposes a template matching method for enhancing and generalizing OSM buildings. Our method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the generalization by searching and matching the most similar template that can be used later to replace the original building. The propose method includes two stages for shape measurement and template matching, in the first step, we employ the turning function as shape descriptor to measure the similarity to find the best candidate template for original buildings. In the second step, we carry out a geometry transformation to adjust the position of the candidate template based on the least squares model. To assess the reasonability and feasibility of the proposed method, experiments on the test area that covers a region around the eastern part of Lund are carried out. With respect to this dataset, we manually create two levels of template libraries, the first level contains the templates in more detail, but the second more abstract. To ensure the individual geometric accuracy, we define the surface distance by comparing the areas of the intersection and union between building and template, and introduce a rejection threshold for it. In this cases, we set the threshold to 0.3 for the first level templates and the correctness is 95.1%. For the second one, we use a smaller value for the threshold, such as 0.4, to obtain a more abstract generalized result, and the correctness is 92.8%. From these experiments, two conclusions can be drawn: firstly, from the perspective of data processing, the template may make data more standard, to some extent overcome the data quality issues of VGI, for example, keeping the corners as a right angle. Secondly, from the perspective of representations, the same template can be used for these similar buildings in a certain region to make their shapes more regular and their scales more uniform. What’s more, for these special shapes, such as alphabetic building, some symbolize templates can be used to enhance the cognitive nature of the map. Through in-depth analysis we found that there are three main aspects that may cause errors: (1) the shape of building is too complicated to find a suitable template in the library; (2) there is a suitable template but a wrong template is found when calculating the similarity distance; and (3) a destructive offset of position and orientation occurs when the transformation is performed. For the first type, the completeness of template library has an impact on the quality of generalization, thus, it should be further enriched and extended in the future, for instance, by considering the situation where buildings are complicated with more than one hole and using other sources of information (e.g., street network) to avoid some potential conflicts. The second and third types of error are what might have been expected because of the actuality that there is no method can achieve full correctness for shape representation and similarity measurement, and some high-performance machine learning methods also can be introduced to identify shape structures and community patterns of buildings, then templates may be applied to building communities, which will greatly expand the scale range of generalization.